D-MAX | DMC-20SEC | Automatic Optical Inspection of Soldering

Chapter 16
Automatic Optical Inspection of Soldering
Mihály Janóczki, Ákos Becker, László Jakab,
Richárd Gróf and Tibor Takács
Additional information is available at the end of the chapter
http://dx.doi.org/10.5772/51699
1. Introduction
Automatic Optical Inspection (AOI) or Automated Visual Inspection (AVI) is a control process.
It evaluates the quality of manufactured products with the help of visual information. Amongst
its several uses, one is the inspection of PWB (Printed Wiring Boards) after their assembling
sequences i.e. paste printing, component placement and soldering. Nowadays, surface mount
technology is the main method of assembly. It can be automated with ease. The increasing
widespread use of SMT (Surface Mount Technology) in PWB assembly results in down-scaling
of component size, increasing of lead count and component density. Parallel to this the latest
manufacturing assembly lines have a very high rate of productivity. Not only is productivity
required but a high quality also is expected. The quality requirements for electronic devices
have already been standardized, e.g. IPC, ANSI-JSTD standards. Modern machines used in
manufacturing lines such as paste printers, component placement machines etc. are capable
of producing significantly better results than those required in normal standards specifica‐
tions. Nowadays, the capability of modern manufacturing machines now reaches 6 σ as usually
applied specification norm and 5 σ for more stringent ones. Even so, manufacturing processes
are still kept under constant supervision. There still occasions when even a modern assembly
line fails to create fully operational devices.
Besides the “classic” electric tests, such as in-circuit-test (ICT) and/or functional tests, there are
in-line inspection possibilities: automatic optical inspection and automatic X-ray inspection
systems. Because of their capabilities and properties, mostly the AOI systems are used as inline quality inspection appliances. The main advantage of these systems is their ability to detect
failures earlier and not only when the product has been assembled. AOI systems can be used
to inspect the quality at each stage of the manufacturing process of the electronic device.
Accordingly, there are real financial advantages by using such systems because the sooner a
© 2013 Janóczki et al.; licensee InTech. This is an open access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
388
Materials Science - Advanced Topics
failure can be detected, the smaller the likelihood of refuse device manufacturing. Because of
component down-scaling and increasing in density, optical inspection is now only possible
with the help of machine vision as opposed to manual inspection.
2. Rise of the AOI systems
Manufacturing electronic devices necessitates the constant controlling and inspection of the
product. Previously, ICT was the main appliance used for this purpose. It inspected electronic
components (e.g. resistor, capacitor etc.), checked for shorts, opens, resistance, capacitance and
other basic quantities. Finally, it checked the proper operation of the whole circuit to show
whether the assembly had been correctly fabricated. It operated by using a bed of measurenails type test fixtures, designed for the current PWB and other specialist test equipment. It
had the following disadvantages. As the dimensions of components were shrinking and the
emplacement density was increasing, the positioning of the test-points became increasingly
difficult. Beds of measure-nails are also relatively expensive and they are PWB-specific. This
problem was however, partially solved by using flying-probe ICT systems (but at the expense
of speed). Another disadvantage of ICT was that only finished product could be examined. It
was able to detect failures but not to prevent them. It is also was not suitable for inspecting the
quality of various assembling technologies. A further disadvantage was also in the case of
functional testing. Extra measurement procedures had to be developed to ensure the enhance‐
ment of the quality of the manufacturing process.
Previously, the quality of solder joints had only been verified by manual visual inspection
(MVI). The disadvantage of manual inspection, which at best was subjective, was that the
tolerance limits were narrower than used in automated machines. A magnifying glass could
help for a while, but as the mounting number of components per unit area exceeded the
capabilities of manual testing, this option was already proving to be difficult or not even
applicable as described in [1]. Because of the rapid development of digital computing, machine
vision and image processing, it was obvious that it was becoming necessary to automate the
process with the help of various high-resolution cameras, novel lighting devices [2], illumi‐
nation techniques [3]-[5] and efficient image processing algorithms. Such state-of-the-art
devices and solutions are described in detail in the following books: [6]-[8].
In cases where the manufacturing of large quantities of precise and high quality products takes
place, the capabilities of production appliances can only be used effectively if the inspections,
after various technological sequences are automated (in-line), are fast and reliable. As a result,
the automatic optical inspection or testing appliances has been developed to replace manual
inspection. The words, Automated Optical Inspection imply that when used in the manufac‐
turing and assembly of PWBs, the nature of the inspection process itself, using digital machine
vision and image processing, will give objective results.
AOI inspects bare and mounted PWBs automatically and uses optical information. It is faster,
more accurate and cheaper than manual inspection. In preparing the parameters for such
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
inspections, parametric test procedures can be used to evaluate the digital image and on the
basis of this they classify the inspected PWB, component or solder joint. Automatic Optical
Inspection systems offer a reliable, flexible, fast and cost-effective solution when inspecting
each step of the manufacturing process. Using AOI systems also has financial advantages.
Detailed calculations show this in [1]. Further works give more reasons why AOI should be
used. Several economic, efficiency and suitability studies have been undertaken about these
systems [9]-[29].
3. Sensors, image capturing methods, structure
In the early 1970s, CCD (Charge Coupled Device) and CMOS (Complementary Metal–Oxide–
Semiconductor) sensors were invented. It presented an opportunity to capture digital images
that could be processed and evaluated by a computer. Machine vision was born. The subse‐
quent exponential development resulted in an infinite number of these applications. One such
development was the automatic optical inspection. Comparison between these sensors is
reported in detailed [30]-[36].
Two kinds of methods exist to capture the images: FOI (Field of Interest) based matrix camera
and line scan camera. The first captures several images on an optimized course, the second
scans the whole surface of PWB. Both have their advantages and disadvantages. Line-scan is
the faster method but the design of a proper source of illumination is more difficult or
sometimes not possible at all because the position of components themselves affects the
efficiency of illumination. If the component is parallel or perpendicular to the scanning line,
the captured image could differ. In case of paste inspection, component positioning is out of
question, so line-scan is better choice. For components and meniscus inspection, FOI is better.
A new FOI generation method is shown in [37].
Basically AOI systems have three main parts: optical unit (illumination, cameras), positioning
mechanism, and control system (Fig. 1).
4. Identifying PWB
AOI appliances identify the PWBs with the help of a separate built-in unit i.e. laser-scanner or
by using its inbuilt functionality. On this basis, machines can decide what inspection is
necessary. According to data contained in a barcode, the AOI system loads the appropriate
inspection program. As barcodes (Fig. 2.a) became more widely used, in some cases the amount
of data that could be stored in them was too limited and this became a barrier to its applicability.
To solve this problem, the so-called ‘matrix codes’ (Fig. 2.b) were developed. In [30] 22 types
of linear barcodes and 48 types of matrix codes are described.
389
390
Materials Science - Advanced Topics
Figure 1. The three main part of an AOI system
(a)
(b)
Figure 2. Example for: a) linear barcode b) DMC
5. Inspection of bare PWBs
There are several possibilities, appliances and algorithms when inspecting bare PWBs
optically. These are able to inspect the copper wire-patterns on a PWB surface with high
precision. Optical inspection gives rapid and reliable results regarding the quality of the
PWB. Electrical detection methods, (e.g. ICT, Flying Probes) are slower and more expen‐
sive. Bare PWB inspecting AOIs have a special name: Automatic Optical Test (AOT)
systems. There are several research and survey papers about this topic [31]-[43] and two
manufacturers now have AOT machines [44]-[49]. In Table I a comparison between these
appliances is shown.
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Manufacturer
Model
Lloyd Doyle Limited
Amistar Automation Inc.
K5L
doutech
Excalibur
phasor
redline
LD 6000
610 x 760
610 x 760
(extended:
915 x 1525)
Board size max.
[mm x mm]
510 x 410
610 x 760
610 x 760
760 x 760
(extended:
1000 x 2000)
Board size min.
[mm x mm]
50 x 50
n.a.
n.a.
n.a.
n.a.
n.a.
Board thickness
[mm]
0.5 - 3.0
n.a.
n.a.
n.a.
n.a.
n.a.
Board warp [mm]
+ 0.5; - 1.0
n.a.
n.a.
n.a.
n.a.
n.a.
Board clearance
top [mm]
50
n.a.
n.a.
n.a.
n.a.
n.a.
Board clearance
bottom [mm]
50
n.a.
n.a.
n.a.
n.a.
n.a.
Camera type
CCD
n.a.
n.a.
n.a.
n.a.
n.a.
Illumination
3-stage LED dome lighting
(Upper: IR; Middle: WH;
Lower: WH)
n.a.
n.a.
n.a.
n.a.
n.a.
View size [mm]
30.4 x 22.8
500 x 635
500 x 610
500 x 635
(extended:
915 x 1980)
500 x 635
(extended:
1000 x 2000)
500 x 610
(extended:
865 x 1475)
Resolution [µm]
19
50
50
60
50
n.a.
Inspection time
Applicability
0.45 sec/screen
20 - 60 sq. m/h
45 sq. m/h
n.a.
missing components, position
shift, rotation error, wrong
components, polarity check,
bridge, character recognition
functional and
cosmetic faults
functional and
cosmetic faults
functional and
cosmetic faults
15 sec @ 3/3
mil line/space;
27 sec @ 2/2
20 - 60 sq. m/h
mil line/space
(for 480 x 600
mm board size)
functional and
cosmetic faults
functional and
cosmetic faults
Table 1. Comparison of Automatic Optical Test (AOT) machines
6. Inspections following SMT sequences
In the SMT assembling process there are three main phases where AOI plays an important
role; after-paste printing, component placement and soldering. In the case of wave, r selective
or partial soldering there are other sequences e.g. glue dispensing, through-hole component
insertion etc. But SMT processes are used mainly for inspection in discussions about this
method of assembly. Possible locations where AOI can be placed in an SMT line are: the postpaste, post-placement or pre-reflow and post reflow (Fig. 3.).
At each location AOI appliances have a special name. These are: Solder Paste Inspection (SPI,
also known as Post-Printing Inspection), Automatic Placement Inspection (API. also known
as Post-Placement Inspection) and Post-Soldering Inspection (PSI). The AOI systems able to
inspect each manufacturing sequence are called: Universal AOI (UAOI). If there is a possibility
that the equipment can inspect the finished product optically, it is then called the Automatic
Final Inspection (AFI).
391
392
Materials Science - Advanced Topics
Figure 3. Possible places of AOI systems
6.1. Solder paste inspection
According to PWB assemblers, it is very important that the quality of the print solder paste is
inspected because it heavily influences the quality of solder joints. In some papers it has been
reported that 52%–71% of SMT defects are related to the printing process [50]-[53]. As failures
can be detected much earlier, this obviously results in cost savings. According to some other
opinions, inspection of the solder paste is not so relevant: “Contrary to the common, frequently
quoted opinion that paste faults represent the primary percentage or 70% of all faults in the
printed circuit assembly process, this detailed analysis shows that those faults amounted to
only 8.3%.” [54].
Special AOI machines are able to inspect the quality of print of the solder paste. It is an important
option because in case of failure, the product can be repaired with minimum cost and with‐
out scrap loss. The size of the print in the 3 dimensions examined (latitude, longitude, alti‐
tude) must fall within the limits specified. To measure these parameters, so-called SPI (Solder
Paste Inspection) machines have been developed. These machines are able to inspect only one
step i.e. paste printing, but they are cheaper than universal AOI machines. As the control of
solder paste presence is one of the easier tasks, then only the width, length and position needs
to be inspected and so several failures can be detected such as bridges [55]. This can be solved
using image capturing (usually with the help of line scan cameras) and subsequent evaluation.
But to measure volume as well the paste thickness is equally as important. Comparison
between 2D and 3D solder paste inspections are reported in [56] and [57]. There are several
possibilities to enable the measurement of paste volume optically: laser scanner [58]-[63];
projected sinusoidal fringe pattern as in [64]; the development of this technique for solder paste
geometry measurement in [65], [66] and some special methods shown in [67]-[69]. Nine
manufacturers offer SPI systems [70]-[82]. Several different solutions have been developed in
these appliances as can be found in the scientific literature, described above. Comparison
between the different methods is shown in Table 2.
height, area,
50 - 610 0.13 volume, registration,
bridge detection
3-5
508 x 503 15 x 15
80.0
50.0
4-5
30
15
height, area,
50 - 500 0.20 volume, registration,
bridge detection
10
810 x 605 15 x 15
80.0
50.0
4-5
30
15
height, area,
50 - 500 0.20 volume, registration,
bridge detection
n.a.
41.0
n.a.
n.a.
10
20
20 - 400 0.37
height, area,
volume, offset,
bridge detection,
shape deformity
n.a.
n.a.
15.0
19.7
n.a.
20
20
20 - 400 0.37
height, area,
volume, offset,
bridge detection,
shape deformity
n.a.
n.a.
n.a.
80.0
80.0
n.a.
n.a. n.a. max. 600 n.a.
height, area,
volume, offset,
bridge detection,
shape deformity
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
10;
15;
20
3
CyberOptics
SE 500
510 x 510
50 x 50
CyberOptics
SE 500-X
Koh Young
Technology
aSPIre
n.a.
n.a.
n.a.
n.a.
Koh Young
Technology
KY-8030
n.a.
n.a.
n.a.
Marantz
Power Spector
510 x 460
50 x 50
Omron
VT-RNS-P
510 x 460
50 x 50
Manufacturer
101 x 40
810 x 610 100 x 100
10;
15;
20
n.a.
n.a.
presence of solder,
insufficient solder,
excessive solder,
solder shifting,
grazing, bridging,
spreading
n.a.
average height,
volume, excessive
deposition,
insufficient solder,
smearing,
misalignment,
bridging
Omron - CKD
VP5000L
510 x 460
50 x 50
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
12
12
Orpro Vision
Symbian P36
508 x 540
n.a.
n.a.
n.a.
n.a.
60
60
4-6
20
20
50 - 300 5.00
Saki
BF-SPIder-M
250 x 330
50 x 60
n.a.
n.a.
n.a.
n.a.
n.a.
3
12
12
height, area,
max. 450 0.40 volume, shift, shape,
spread, bridge
Saki
BF-SPIder-L
460 x 500
50 x 60
n.a.
n.a.
n.a.
n.a.
n.a.
3-5
12
12
height, area,
max. 450 0.40 volume, shift, shape,
spread, bridge
ScanINSPECT
SPI
457 x 508
50 x 50
n.a.
419 x 508
n.a.
n.a.
n.a.
n.a.
n.a. n.a.
ScanCAD
n.a.
Measurement types
Typical inspection speed @
unload and fiducial find [sec]
20
508 x 508
Height resolution [µm]
Typical inspection speed @
high resolution [sq. cm/sec]
40
SE 300 Ultra
Paste height range [µm]
Typical inspection speed @
high speed [sq. cm/sec]
3-4
CyberOptics
X and Y pixel size @ high
speed [µm]
X and Y pixel size @ high
resolution [µm]
Maximum pad size in field of
view [mm]
16.0
Maximum board weight [kg]
29.0
Board size min. [mm x mm]
5 x 10
Board size max. [mm x mm]
508 x 503
Model
Maximum inspection area
[mm]
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
n.a.
n.a.
n.a.
n.a.
TRI Innovation TR7006/L/LL-20
330 x 280
50 x 50
3
n.a.
n.a.
98.8
24.7
n.a.
20
20
40 - 600
n.a.
n.a.
TRI Innovation TR7006/L/LL-16
510 x 460
50 x 50
5
n.a.
n.a.
63.2
15.8
n.a.
16
16
32 - 480
n.a.
n.a.
TRI Innovation TR7006/L/LL-12
660 x 610
50 x 50
10
n.a.
n.a.
35.5
8.8
n.a.
12
12
24 - 360
n.a.
n.a.
TRI Innovation
TR7066-20
510 x 460
50 x 50
3
n.a.
n.a.
153.6
23.0
n.a.
20
20
40 - 600
n.a.
n.a.
TRI Innovation
TR7066-16
510 x 460
50 x 50
3
n.a.
n.a.
122.9
18.4
n.a.
16
16
32 - 480
n.a.
n.a.
TRI Innovation
TR7066-12
510 x 460
50 x 50
3
n.a.
n.a.
92.2
13.8
n.a.
12
12
24 - 360
n.a.
n.a.
Viscom
S3088-II QS
450 x 350
n.a.
n.a.
n.a.
n.a.
110
110
n.a.
22
22
n.a.
n.a.
n.a.
n.a.
height, area,
volume, bridge,
shape, position
Vi Technology
Table 2.
3D-SPI
510 x 460
n.a.
n.a.
n.a.
n.a.
20.0
20.0
n.a.
n.a. n.a.
n.a.
393
Volume repeatability on a
circuit board
Gage R&R
n.a.
n.a.
n.a.
Measurement types
Volume repeatability on a
certification target
Board warp
Conveyor height [mm]
Conveyor speed [mm/sec]
Model
Materials Science - Advanced Topics
Manufacturer
394
CyberOptics
SE 300 Ultra
150 - 450 889 - 990
<2% of PCB
diagonal or
6.35mm
total
CyberOptics
SE 500
150 - 450 810 - 970
<2% of PCB
diagonal or
< 1%; 3 σ
6.35mm
total
<3%; 3 σ
<< 10%; 6 σ
height, area,
volume, registration,
bridge detection
CyberOptics
SE 500-X
150 - 450 810 - 970
<2% of PCB
diagonal or
< 1%; 3 σ
6.35mm
total
<3%; 3 σ
<< 10%; 6 σ
height, area,
volume, registration,
bridge detection
Koh Young
Technology
aSPIre
n.a.
830 - 970
± 5.0 mm
< 1%; 3 σ
<3%; 3 σ
<< 10%; 6 σ
height, area,
volume, offset,
bridge detection,
shape deformity
Koh Young
Technology
KY-8030
n.a.
870 - 970
± 3.5 mm
< 1%; 3 σ
<3%; 3 σ
<< 10%; 6 σ
height, area,
volume, offset,
bridge detection,
shape deformity
Marantz
Power Spector
n.a.
830 - 970
± 5.0 mm
n.a.
n.a.
n.a.
height, area,
volume, offset,
bridge detection,
shape deformity
Omron
VT-RNS-P
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
presence of solder,
insufficient solder,
excessive solder,
solder shifting,
grazing, bridging,
spreading
height, area,
volume, registration,
bridge detection
Omron - CKD
VP5000L
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
average height,
volume, excessive
deposition,
insufficient solder,
smearing,
misalignment,
bridging
Orpro Vision
Symbion P36
n.a.
870 - 930
+ 3.0 mm;
- 6.0 mm
n.a.
n.a.
< 10%
n.a.
Saki
BF-SPIder-M
n.a.
max. 900
n.a.
< 1%; 3 σ
n.a.
< 10%
height, area,
volume, shift, shape,
spread, bridge
Saki
BF-SPIder-L
n.a.
max. 900
n.a.
< 1%; 3 σ
n.a.
< 10%
height, area,
volume, shift, shape,
spread, bridge
ScanCAD
ScanINSPECT
SPI
TRI Innovation TR7006/L/LL-20
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
TRI Innovation TR7006/L/LL-16
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
TRI Innovation TR7006/L/LL-12
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
TRI Innovation
TR7066-20
TRI Innovation
TR7066-16
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
TRI Innovation
TR7066-12
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
Viscom
S3088-II QS
n.a.
850 - 960
n.a.
n.a.
n.a.
n.a.
n.a.
< 10%
height, area,
volume, bridge,
shape, position
Vi Technology
3D-SPI
n.a.
max. 950
± 3.5 mm
Table 3. Comparison of Solder Paste Inspection (SPI) machines
n.a.
n.a.
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
6.2 Automatic placement inspection
Inspection of the PWB after component placement is the next possibility. With this method
possible placement failures can be detected and some defective paste printing phenomena as
well. If there is a sign or mark on the component, it can be read and identified with the help
of modern image processing algorithms even if it has more than one different-looking label
type. APIs (Table III) are able to measure most parameters of components objectively e.g. X-Y
shift, rotation, polarity, labels, size etc. [83]-[90]. Four manufacturers have this special appli‐
ance in stock. [91]-[94].
Manufacturer
BeamWorks
Landrex
Omron
Viscom
Model
Inspector cpv
Optima II 7301 Express
VT-RNS-Z
S3054QV
12 x 9 mm @ 15 µm; 48 x 36
mm @ 73 µm
10 x 10 mm; 15 x 15 mm
n.a.
1280 x 1024 pixel
Pixel size [µm]
15; 73
n.a.
10; 15; 20
10; 22
Depth of field (max. component
height for inspection) [mm]
10; 15
n.a.
n.a.
n.a.
1
1 vertical, 4 angled
1
1
oblique ring, white LED light
n.a.
ring-shaped RGB LED
n.a.
508 x 406
609 x 558
510 x 460
443 x 406
370 x 406
Field of view
Number of cameras
Lighting method
Board size max. @ single board
operation [mm x mm]
Board size max. @ dual board
operation [mm x mm]
none
none
none
Board size min. [mm]
40 x 28
51 x 76
50 x 50
n.a.
Board thickness [mm]
0.8 - 3.2
n.a.
n.a.
n.a.
Conveyor height [mm]
n.a.
n.a.
n.a.
850 - 960
4
n.a.
n.a.
3
Board edge clearance top [mm]
25; 37
63
20
35
Board edge clearance bottom
[mm]
25; 37
63
50
50
n.a.
250 ms/screen @ 10 sq. mm
field of view
20 - 30
Board edge clearance [mm]
Inspection speed [sq. cm/sec]
2 @ 12 x 9 mm field of view
Applicability
missing component, wrong
component, polarity check,
offset, skew
missing components, misoriented
presence of solder, component
components, extra components,
shifting, polarity error, missing
component placement, tombstoned and
components, wrong
bill boarded components, lifted leads,
components, solder balls,
insufficient solder and excess solder,
skewing, bridging, foreign
wrong part, through hole pins
objects
n.a.
Table 4. Comparison of Automatic Placement Inspection (API) machines
6.3 Post soldering inspection
Most manufacturers agree from a strategic point of view, that optical inspection after soldering
has been completed should not be missed out. At the very least, the defective products must
be eliminated because many failures are generated during soldering: “Forty-nine percent of
the true faults were detectable only after soldering. These consisted of component and
soldering faults. Forty-eight percent of the optically recognizable faults could not be recog‐
nized electrically.” [54].
395
396
Materials Science - Advanced Topics
In consequence, this is the most important part of the AOI inspection process. Most scientific
papers are preoccupied with this subject [95]-[123]. The quality of the solder joint (and the
soldering process) can be inspected with the methods described in this section. The quality of
the solder joints is determined from geometric and optical properties of the solder meniscus.
These parameters determine the reflection properties of the meniscus which is formed from
the liquid alloy during the soldering process. After cooling, the meniscus becomes solid and
reflects illumination which means that we can evaluate it (Fig.5, Fig. 6). From these reflection
patterns and with the help of image processing algorithms we are able to determine the quality
of the solder joints.
Figure 4. Schematic of the meniscus
Figure 5. Reflection pattern on meniscus model with white ring illumination
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Figure 6. Reflection pattern on meniscus model with RGB ring illumination
Using reflection patterns is the basis of all papers that have been published in this field of
study. There are two solutions: gray-scale or colour inspection. Supplier and appliances are
shown in Table IV [124]-[126].
One interesting area is wave soldering. It needs different types of algorithms because of the
circular solder shape and the pin. Some solutions for this kind of inspection are reported in
[127]-[129]. A summary of possible failures and appliances that can detect them are shown in
Table V.
6.4. Combined appliances
There are systems that are able to inspect more sequences. These are combined systems, namely
API&PSI [140]-[146] (Table VI).
And the all-in-one machines are the UAOI systems, detailed: SPI&API&PSI [147]-[163] (Table
VII).
397
398
Materials Science - Advanced Topics
Manufacturer
Viscom
TRI Innovation
TR7530
S3016
S3054QC
Field of view (orthogonal
camera) [pixel]
1024 x 768
2752 x 2048 @ 55 x 43 mm
672 x 512
Pixel size (orthogonal
camera) [µm]
10; 15; 20
22; 10
22; 10
1
4
1, 2 or 4
Resolution (angled view
camera) [µm/pixel]
n.a.
15
n.a.
Number of cameras (angled
view)
n.a.
4
n.a.
ultra-low angle, multisegment, RGB LED
lighting
n.a.
n.a.
Model
Number of cameras
(orthogonal)
Illumination
Inspection speed
72 sq. cm/sec @ 20 µm;
typical connector with 100 pins typical connector with 100 pins
40 sq. cm/sec @ 10 µm;
15 sec
15 sec
18 sq. cm/sec @ 10 µm
Board size max. [mm]
400 x 300
430 x 406
Board size min. [mm]
50 x 50
n.a.
n.a.
Board weight max. [kg]
3
n.a.
n.a.
Conveyor height [mm]
n.a.
850 - 960
850 - 960
Board edge clearance [mm]
3
3
3
Board clearance top [mm]
25
50
15
Board clearance bottom
[mm]
40
35
50
selective and special solder
joints
selective and special solder
joints
missing component,
tombstone, billboard,
polarity, skew, marking,
defective, insufficient and
Applicability
excess solder, bridge,
trough hole pin, lifted
lead, golden finger,
scratch, blur
Table 5. Comparison of Post Soldering Inspection (PSI) machines
443 x 406
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
UAOI
SPI
past e
API
pr e r ef low
inspect ion
component in past e
missing past e
complet eness or / and
volume of solder past e
whet her it is not cover ed by component
misalignment
past e br idge
smear ed solder past e
cont aminat ion
whet her it is not cover ed by component
whet her it is not cover ed by component
missing component
component posit ion (x-y
shif t , r ot at ion, f ace lif t )
polar it y
damaged component
unsolder ed component
insuf f icient solder j oint
solder br idge
lif t ed lead
t ombst one
missing adhesive
smear ed adhesive on pad
missing pin end
insuf f icient pin solder
dewet t ing
PWB r egist r at ion
Table 6. Possible failures and appliances
PSI
post r ef low
API
PSI
adhesive
pr e wave
post wave
inspect ion
component in adhesive
399
Component
inspection
Printing/paste
inspection
Distinction
principles
Distinction
parameters
Camera type
Camera field of
view/resolution
Lens
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
5MP color camera
7 - 17 µm
n.a.
Illumination
Board movement
Model
Camera movement
Materials Science - Advanced Topics
Manufacturer
400
programmable
variable LED strobe
lighting, proprietary
multi-color
illumination
Machine
Vision
Products
Supra E
Marantz
iSpector
HDL 350L
X+Y
direction
presence,
stationary
polarity, offset,
during
correctness,
inspection
soldering
offset,
smearing,
bridges,
uniformity
synthetic
telecentric omnidirectional triple
imaging, brightness,
lens with
LED rings; side,
UXGA CCD digital 32 x 24 mm @ 20 µm;
hue,
spectral
built in
main, line sourced
camera with
40 x 30 mm @ 25 µm;
analysis, saturation
prism for DOAL (diffused on
CameraLink
16 x 12 mm @ 10 µm
grayscale via filters
DOAL
axis lighting
limits
lighting
(coaxial))
Marantz
iSpector
HDL 650L
X+Y
direction
presence,
stationary
polarity, offset,
during
correctness,
inspection
soldering
offset,
smearing,
bridges,
uniformity
synthetic
telecentric omnidirectional triple
imaging, brightness,
lens with
LED rings; side,
UXGA CCD digital 32 x 24 mm @ 20 µm;
spectral
hue,
built in
main, line sourced
camera with
40 x 30 mm @ 25 µm;
analysis, saturation
prism for DOAL (diffused on
CameraLink
16 x 12 mm @ 10 µm
grayscale via filters
DOAL
axis lighting
limits
lighting
(coaxial))
Marantz
iSpector
HML 350L
X+Y
direction
presence,
stationary
polarity, offset,
during
correctness,
inspection
soldering
offset,
smearing,
bridges,
uniformity
synthetic
imaging, brightness,
UXGA CCD digital 32 x 24 mm @ 20 µm;
high
spectral
hue,
camera with
40 x 30 mm @ 25 µm; resolution
analysis, saturation
CameraLink
16 x 12 mm @ 10 µm telecentric
grayscale via filters
limits
omnidirectional 4angle LED: RGBDOAL (coaxial)
Marantz
iSpector
HML 650L
X+Y
direction
presence,
stationary
polarity, offset,
during
correctness,
inspection
soldering
offset,
smearing,
bridges,
uniformity
synthetic
imaging, brightness,
UXGA CCD digital 32 x 24 mm @ 20 µm;
high
spectral
hue,
camera with
40 x 30 mm @ 25 µm; resolution
analysis, saturation
CameraLink
16 x 12 mm @ 10 µm telecentric
grayscale via filters
limits
omnidirectional 4angle LED: RGBDOAL (coaxial)
stationary
during
inspection
presence and
absence of
components,
placement
X+Y+Z
direction accuracy and
polarity, optical
character
recognition
insufficient
solder,
tombstone,
billboard,
coplanarity,
lifted leads,
shorts
n.a.
n.a.
4MP XGA high
resolution top and
4 side cameras
with symmetric
image acquisition
and color
capability
Orpro
Vision
Symbion
S36
n.a.
n.a.
axial, direct, diffuse
and RGB multi-color
illumination
TRI
7500
Innovation
n.a.
n.a.
missing,
tombstone,
billboard,
polarity, shift
insufficient
solder,
excess
solder,
bridge
n.a.
n.a.
1 top view XGA
3CCD camera @
1024 x 768 pixel, 10 µm; 15 µm; 20 µm;
4 angle view XGA
25 µm
mono camera &
1024 x 768 pixel
n.a.
multi segment, multi
angle LED, RGB+W
TRI
7500L
Innovation
n.a.
n.a.
missing,
tombstone,
billboard,
polarity, shift
insufficient
solder,
excess
solder,
bridge
n.a.
n.a.
1 top view XGA
3CCD camera @
1024 x 768 pixel, 10 µm; 15 µm; 20 µm;
4 angle view XGA
25 µm
mono camera &
1024 x 768 pixel
n.a.
multi segment, multi
angle LED, RGB+W
n.a.
n.a.
1 top view XGA
3CCD camera @
1024 x 768 pixel, 10 µm; 15 µm; 20 µm;
4 angle view XGA
25 µm
mono camera &
1024 x 768 pixel
n.a.
multi segment, multi
angle LED, RGB
(coaxial lighting
optional)
n.a.
n.a.
2MP color CCD
camera
24.8 x 18.6 mm @ 15.5
µm; 17.6 x 13.2 mm @
11 µm
n.a.
high intensity white
LED
n.a.
n.a.
line color CCD
camera
10 µm
n.a.
LED lighting system
TRI
7550
Innovation
Sony
Saki
Table 7.
SI-V200
BF-Tristar
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
insufficient
solder,
missing,
excess
tombstone,
solder,
billboard,
bridge,
polarity, skew, trough hole
marking,
pins, lifted
defective
lead, golden
finger
scratch, blur
inaccurate
mounting,
reversed,
polarity
missing,
solder,
bridging,
solder
quantity
presence and
absence of
components,
misalignment, absence,
tombstone,
insufficient
reverse,
solder, fillet
polarity, bridge,
defect
foreign
material, lifted
lead, lifted chip
Movement speed
[mm/sec]
Inspection capacity
typical
Conveyor speed
[mm/sec]
n.a.
90 sq. cm/sec
n.a.
350 x 250
50 x 50
720
1500 cps/min
35,
55
650 x 550
50 x 50
720
30
35,
55
350 x 250
50 x 50
pixel
related
feedback
loop
30
35,
55
650 x 550
01005 (0.4 x 0.2
mm) down to 0.3
mm pitch
n.a.
90
90
TRI
7500
Innovation
n.a.
n.a.
50
TRI
7500L
Innovation
n.a.
n.a.
TRI
7550
Innovation
n.a.
Conveyor height
[mm]
Board clamping
Board thickness
[mm]
n.a.
10 - 500 830 - 950
ruler blade, top &
edge clamping,
sensor stopper
0.6 - 4.0
1500 cps/min
10 - 500 830 - 950
ruler blade, top &
edge clamping,
sensor stopper
0.6 - 4.0
720
1500 cps/min
10 - 500
n.a.
ruler blade, top &
edge clamping,
sensor stopper
0.6 - 4.0
50 x 50
720
1500 cps/min
10 - 500
n.a.
ruler blade, top &
edge clamping,
sensor stopper
0.6 - 4.0
550 x 470
n.a.
n.a.
40 sq. cm/sec
n.a.
n.a.
n.a.
n.a.
50
510 x 460
n.a.
n.a.
110 sq. cm/sec @ 25 µm;
72 sq. cm/sec @ 20 µm;
40 sq. cm/sec @ 15 µm;
18 sq. cm/sec @ 10 µm;
n.a.
n.a.
n.a.
max. 4.0
50
50
660 x 610
n.a.
n.a.
110 sq. cm/sec @ 25 µm;
72 sq. cm/sec @ 20 µm;
40 sq. cm/sec @ 15 µm;
18 sq. cm/sec @ 10 µm;
n.a.
n.a.
n.a.
max. 4.0
n.a.
40
40
540 x 460
50 x 50
n.a.
110 sq. cm/sec @ 25 µm;
72 sq. cm/sec @ 20 µm;
40 sq. cm/sec @ 15 µm;
18 sq. cm/sec @ 10 µm;
n.a.
n.a.
n.a.
0.6 - 4.0
0402 @ high
resolution, 0603
@ normal
resolution
n.a.
20
40
460 x 510
40 x 50
n.a.
0.27 sec/frame
n.a.
n.a.
n.a.
0.4 - 2.0
n.a.
n.a.
30
30
250 x 330
50 x 70
n.a.
20 sec / 250 x 350 mm
n.a.
900
n.a.
0.6 - 2.5
Machine
Vision
Products
Supra E
0201 and 01005
(0.4 x 0.2 mm)
0.5 µm
Marantz
iSpector
HDL 350L
01005 (0.4 x 0.2
mm @ 10 µm)
pixel
related
feedback
loop
40
35,
55
Marantz
iSpector
HDL 650L
01005 (0.4 x 0.2
mm @ 10 µm)
pixel
related
feedback
loop
40
Marantz
iSpector
HML 350L
01005 (0.4 x 0.2
mm @ 10 µm)
pixel
related
feedback
loop
Marantz
iSpector
HML 650L
01005 (0.4 x 0.2
mm @ 10 µm)
Orpro
Vision
Symbion
S36
Sony
SI-V200
Saki
BF-Tristar
Board size max.
[mm x mm]
n.a.
Board clearance
top [mm]
Board clearance
bottom [mm]
n.a.
Minimum
inspection
component size
Model
Position accuracy
n.a.
Manufacturer
Board size min.
[mm x mm]
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
n.a. n.a. 508 x 508
Table 8. Comparison of combined (API&PSI) machines
401
Amistar
Automation
Inc.
K2
Amistar
Automation
Inc.
K2L
CyberOptics
Flex HR 8
CyberOptics
Flex HR 12
CyberOptics
Flex HR 11
Machine
Vision
Products
Ultra IV
n.a.
Mirtec
MV-7
n.a.
n.a.
Mirtec
MV-7L
n.a.
n.a.
Mirtec
MV-7xi
n.a.
n.a.
Mirtec
MV-7U
n.a.
n.a.
Omron
Omron
Saki
Saki
Viscom
Viscom
Viscom
Viscom
Viscom
Vi Technology
4 megapixel digital
camera
44.7 x 32.8
19; 12
n.a.
n.a.
n.a.
4 megapixel digital
camera
44.7 x 32.8
19; 12
n.a.
n.a.
n.a.
4 megapixel digital
camera
44.7 x 32.8
21; 12
n.a.
n.a.
n.a.
CCD
30.4 x 22.8
19
n.a.
n.a.
n.a.
auto-adjust 3-stage
LED dome lighting
(Upper: IR; Middle:
WH; Lower: WH)
CCD
30.4 x 22.8
19
n.a.
n.a.
n.a.
auto-adjust 3-stage
LED dome lighting
(Upper: IR; Middle:
WH; Lower: WH)
5 megapixel color
CMOS camera
n.a.
17
5
n.a.
n.a.
fluorescent white light
5 megapixel color
CMOS camera
n.a.
17
8
n.a.
n.a.
fluorescent white light
5 megapixel color
CMOS camera
n.a.
17
11
n.a.
n.a.
fluorescent white light
n.a.
n.a.
8 - 17
n.a.
n.a.
n.a.
programmable variable
LED strobe lighting,
proprietary multi-color
illumination
1.3, 2 or 4 megapixel
digital color camera
1.3, 2 or 4 megapixel
digital color camera
1.3, 2 or 4 megapixel
digital color camera
1.3, 2 or 4 megapixel
digital color camera
14.0 x 10.5 to
37.2 x 37.2
14.0 x 10.5 to
37.2 x 37.2
14.0 x 10.5 to
37.2 x 37.2
14.0 x 10.5 to
37.2 x 37.2
presence of solder, wrong
presence of solder, wrong
components, missing
components, missing components,
components, bridging,
bridging, tombstone, component
3-CCD camera
component shifting, lead shifting, fillet, wettability, lead bending,
bending
adhesive, solder balls
presence/absence,
presence/absence of solder,
skewed, shifted, wrong
excessive solder, insufficient solder,
triple element CCD
component, un-inserted, blow holes, wettability, bridges, solder
VT-WIN II
camera
upside-down/backward,
balls, skewed, shifted, wrong
polarity, lead bent
component, polarity, lead bent
presence/absence, tombstone,
presence/absence,
reverse, polarity, bridge, foreign
BF-Frontier
misalignment, polarity,
material, absence of solder,
line color CCD camera
bridge
insufficient solder, lifted lead, lifted
chip, fillet
presence/absence, tombstone,
presence/absence,
reverse, polarity, bridge, foreign
misalignment, polarity,
material, absence of solder,
line color CCD camera
BF-Planet-X
bridge
insufficient solder, lifted lead, lifted
chip, fillet
S3088-III
n.a.
n.a.
megapixel
S3088-II
n.a.
n.a.
megapixel
S6056-ST1
n.a.
n.a.
megapixel
S6056-DS1W
n.a.
n.a.
megapixel
S6056-DS2W
n.a.
n.a.
megapixel
VT-RNS-S
3K Series
n.a.
n.a.
Illumination
Post-reflow
inspection
n.a.
Number of
angular
cameras
Medalist
sj5000
Angular
camera
resolution
[µm]
Agilent
Technologies
Number of
orthogonal
cameras
Medalist
SJ50 Series
3 XL
Orthogonal
camera
resolution
[µm]
Agilent
Technologies
missing, offset, skewed, polarity,
missing, offset, 2D paste,
billboard, tombstone, lifted/bent leads,
skewed, polarity, bridging,
excess/insufficient solder, bridging,
wrong part, traceability
wrong part, traceability
missing, offset, skewed, polarity,
missing, offset, 2D paste,
billboard, tombstone, lifted/bent leads,
skewed, polarity, bridging,
excess/insufficient solder, bridging,
wrong part, traceability
wrong part, traceability
missing, offset, 2D paste,
missing, offset, skewed, polarity,
skewed, polarity, bridging, billboard, tombstone, lifted/bent leads,
billboard, wrong part ,
excess/insufficient solder, bridging,
extra part, traceability
wrong part, traceability
missing components, position shift,
rotation error, wrong components,
position shift, blur, solder
polarity check, bridge, character
area, bridge
recognition, solder quantity, lifted
leads
missing components, position shift,
rotation error, wrong components,
position shift, blur, solder
polarity check, bridge, character
area, bridge
recognition, solder quantity, lifted
leads
missing, polarity, billboard, flipped,
wrong part, gross body and lead
missing, polarity, billboard,
flipped, wrong part, gross damage, gold-finger contamination,
body and lead damage, tombstone, solder bridge, opens, lifted
gold-finger contamination, leads, wettability, excess/insufficient
solder, debris
missing, polarity, billboard, flipped,
wrong part, gross body and lead
missing, polarity, billboard,
flipped, wrong part, gross damage, gold-finger contamination,
body and lead damage, tombstone, solder bridge, opens, lifted
gold-finger contamination, leads, wettability, excess/insufficient
solder, debris
missing, polarity, billboard, flipped,
missing, polarity, billboard,
wrong part, gross body and lead
flipped, wrong part, gross damage, gold-finger contamination,
body and lead damage, tombstone, solder bridge, opens, lifted
gold-finger contamination, leads, wettability, excess/insufficient
solder, debris
Orthogonal
camera field
of view [mm]
Medalist
SJ50 Series
3
Camera type
Agilent
Technologies
Pre-reflow
inspection
Model
Materials Science - Advanced Topics
Manufacturer
402
multiple color, multiple
angle, multiple
segment ED lighting
head, auto-calibration
multiple color, multiple
angle, multiple
segment ED lighting
head, auto-calibration
multiple color, multiple
angle, multiple
segment ED lighting
head, auto-calibration
9.8 - 18.2
n.a.
n.a.
4
n.a.
9.8 - 18.2
n.a.
n.a.
4
n.a.
9.8 - 18.2
n.a.
n.a.
4
n.a.
9.8 - 18.2
n.a.
n.a.
4
n.a.
n.a.
10; 15; 20
n.a.
n.a.
n.a.
ring-shaped LED (red,
green, blue)
n.a.
10; 13; 15;
20; 25; 30;
35; 50
n.a.
n.a.
n.a.
3 ring-shaped LED
arrays with automatic
brightness control
n.a.
18
n.a.
n.a.
n.a.
LED lighting system
10
n.a.
n.a.
n.a.
LED lighting system
57.6 x 43.5
57.6 x 43.5
57.6 x 43.5
57.6 x 43.5
57.6 x 43.5
23.4; 11.7
23.4; 11.7
23.4; 11.7
23.4; 11.7
23.4; 11.7
4
4
4
4
4
16.1; 8.05
16.1; 8.05
16.1; 8.05
16.1; 8.05
16.1; 8.05
4; 8
4; 8
4; 8
4; 8
4; 8
n.a.
n.a.
n.a.
n.a.
n.a.
i-LITE (red, green,
blue); axial and
peripheral
i-LITE (red, green,
blue); axial and
peripheral
i-LITE (red, green,
blue); axial and
peripheral
amber, green, blue
n.a.
1620 x 1220 pixel;
2352 x 1728 pixel
42.1 x 31.7;
61.1 x 44.9
8 - 12
n.a.
n.a.
n.a.
n.a.
1620 x 1220 pixel;
2352 x 1728 pixel
42.1 x 31.7;
61.1 x 44.9
8 - 12
n.a.
n.a.
n.a.
Vi Technology
5K Series
Vi Technology
7K Series
n.a.
n.a.
1620 x 1220 pixel;
2352 x 1728 pixel
42.1 x 31.7;
61.1 x 44.9
8 - 12
n.a.
n.a.
n.a.
Vi Technology
Vi-5000
n.a.
n.a.
1360 x 1040 pixel
44.5 x 33.6
12
n.a.
n.a.
n.a.
green, white, blue,
axial and peripheral
Vi Technology
Vi-5000-2
n.a.
n.a.
1600 x 1152 pixel
41.6 x 29.9
8
n.a.
n.a.
n.a.
Vi Technology
Vi-5000-3
n.a.
n.a.
2048 x 2048 pixel
53.2 x 53.2
8
n.a.
n.a.
n.a.
green, white, blue
YES Tech
YTV-F1
position, missing, wrong,
polarity, skew
polarity, skew, tombstone, bent lead,
lifted, bridging, open solder,
insufficient, short, solder balls
Multiple Thin Camera
megapixel color topdown viewing camera
@ 1280 x 1024 pixel
n.a.
25; 12
n.a.
n.a.
n.a.
LED top light,
proprietary bi-color
multiangle LED lighting
YES Tech
YTV-F1S
position, missing, wrong,
polarity, skew
n.a.
25; 12
n.a.
n.a.
n.a.
LED top light,
proprietary bi-color
multiangle LED lighting
YES Tech
YTV-M1
position, missing, wrong,
polarity, skew
n.a.
25; 12
n.a.
n.a.
n.a.
proprietary Fusion
Lighting
Table 9.
Multiple Thin Camera
megapixel color topdown and 4 side
viewing camera @
1280 x 1024 pixel
YESTech 3 Megapixel
polarity, skew, tombstone, bent lead, Thin Camera top-down
viewing camera and
lifted, bridging, open solder,
insufficient, short, solder balls
telecentric lens @
2048 x 1536 pixel
polarity, skew, tombstone, bent lead,
lifted, bridging, open solder,
insufficient, short, solder balls
Board thickness
[mm]
Board weight max.
[kg]
Dual lane capable
n.a.
n.a.
n.a.
0.5 - 4.0
3
Yes
n.a.
41 sq. cm/sec @ pre-reflow;
32 sq. cm/sec @ post reflow
620 x 620
75 x 50
n.a.
n.a.
n.a.
1.5 - 15
13
n.a.
n.a.
Agilent
Technologies
Medalist
sj5000
41 sq. cm/sec @ pre-reflow;
32 sq. cm/sec @ post reflow
510 x 510
50 x 50
n.a.
n.a.
n.a.
0.5 - 4.0
3
Yes
n.a.
K2
0.25 sec/screen
330 x 250
50 x 50
+ 0.5; - 1.0
28
25
0.5 - 2.0
n.a.
n.a.
n.a.
K2L
0.25 sec/screen
485 x 410
50 x 50
+ 0.5; - 1.0
28
25
0.5 - 2.0
n.a.
n.a.
n.a.
Flex HR 8
50 sq. cm/sec
203 x 508
110 x 63
± 0.7
32
3
n.a.
n.a.
n.a.
813 - 965
Amistar
Automation Inc.
Amistar
Automation Inc.
CyberOptics
Conveyor height
[mm]
Board clearance
bottom [mm]
50 x 50
Medalist SJ50
Series 3 XL
Board clearance
top [mm]
510 x 510
Agilent
Technologies
Board warp [mm]
41 sq. cm/sec @ pre-reflow;
32 sq. cm/sec @ post reflow
Board size min.
[mm x mm]
Medalist SJ50
Series 3
Board size max.
[mm x mm]
Inspection capacity
typical
Agilent
Technologies
Manufacturer
Model
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
CyberOptics
Flex HR 12
50 sq. cm/sec
305 x 508
110 x 63
± 0.7
32
3
n.a.
n.a.
n.a.
813 - 965
CyberOptics
Flex HR 11
50 sq. cm/sec
457 x 508
110 x 63
± 0.7
32
3
n.a.
n.a.
n.a.
813 - 965
Machine Vision
Products
Mirtec
Ultra IV
90 sq. cm/sec
500 x 546
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
MV-7
4.94 sq. mm/sec
350 x 250
50 x 50
n.a.
25 - 45
50.8
n.a.
n.a.
n.a.
n.a.
Mirtec
MV-7L
4.94 sq. mm/sec
500 x 400
50 x 50
n.a.
25 - 45
50.8
n.a.
n.a.
n.a.
n.a.
Mirtec
MV-7xi
4.94 sq. mm/sec
510 x 460
50 x 50
n.a.
25 - 45
50.8
n.a.
n.a.
n.a.
n.a.
Mirtec
MV-7U
4.94 sq. mm/sec
660 x 510
50 x 50
n.a.
25 - 45
50.8
n.a.
n.a.
n.a.
n.a.
Omron
VT-RNS-S
0.25 sec/screen @ 10 sq. mm
field of view
510 x 460
50 x 50
n.a.
20 - 40
40 - 50
n.a.
n.a.
n.a.
n.a.
VT-WIN II
0.40 sec/screen
50 x 50
n.a.
Saki
BF-Frontier
24 sec/screen
460 x 500
50 x 60
± 0.2
40
40
0.6 - 2.5
n.a.
n.a.
max. 900
Saki
BF-Planet-X
23 sec/screen
250 x 330
50 x 60
n.a.
20
30
0.6 - 2.5
n.a.
n.a.
max. 900
Viscom
S3088-III
20 - 40 sq. cm/sec
508 x 508
n.a.
n.a.
35
40
n.a.
n.a.
n.a.
850 - 960
Viscom
S3088-II
20 - 40 sq. cm/sec
450 x 350
n.a.
n.a.
35
40
n.a.
n.a.
n.a.
850 - 960
Omron
460 x 510
50
50
0.3 - 4.0
n.a.
n.a.
n.a.
Viscom
S6056-ST1
20 - 40 sq. cm/sec
457 x 356
n.a.
n.a.
35
60
n.a.
n.a.
n.a.
830 - 960
Viscom
S6056-DS1W
20 - 40 sq. cm/sec
457 x 356
n.a.
n.a.
35
60
n.a.
n.a.
n.a.
830 - 960
Viscom
830 - 960
S6056-DS2W
40 - 80 sq. cm/sec
457 x 356
n.a.
n.a.
35
60
n.a.
n.a.
n.a.
Vi Technology
3K Series
4 - 20 ms
458 x 406
50 x 50
n.a.
34
34
0.7 - 4.0
3
Yes
n.a.
Vi Technology
5K Series
4 - 20 ms
533 x 533
50 x 50
n.a.
34
60
0.5 - 4.0
3
n.a.
n.a.
Vi Technology
7K Series
4 - 20 ms
533 x 610
50 x 50
n.a.
34
60
0.5 - 4.0
3
Yes
n.a.
Vi Technology
Vi-5000
4 - 20 ms
508 x 458
50 x 50
n.a.
34
40
0.7 - 5.0
7
n.a.
n.a.
Vi Technology
Vi-5000-2
508 x 458
50 x 50
40
0.7 - 5.0
Vi Technology
Vi-5000-3
4 - 20 ms
508 x 458
50 x 50
n.a.
34
40
0.7 - 5.0
7
n.a.
n.a.
YTV-F1
35 sq. cm/sec
4 - 20 ms
560 x 510
n.a.
n.a.
n.a.
50
34
50
n.a.
n.a.
n.a.
n.a.
50
n.a.
n.a.
n.a.
n.a.
50
n.a.
n.a.
n.a.
max. 950
YES Tech
YES Tech
YTV-F1S
35 sq. cm/sec
560 x 510
n.a.
n.a.
50
YES Tech
YTV-M1
35 sq. cm/sec
350 x 250
50 x 50
n.a.
25
7
n.a.
n.a.
Table 10. Comparison of Universal Automatic Optical Inspection (UAOI) machines
Assuming that the component is fully operational, these systems practically are able to prove
that the whole circuit board is working correctly thus replacing the ICT. However, because
they are usually connected to SPC (Statistical Process Control) servers, they can also provide
much information about the SMT process itself and provides help as to how to improve it.
But of course there are disadvantages to using AOI systems. They are not able to inspect hidden
failures such as soldered BGA (Ball Grid Array) bumps and usually the parameters of
inspection algorithms cannot be adjusted perfectly. So from time to time they do not detect
real failures which are called ‘slip-through failures’. These are the most significant malfunc‐
tions during the operation of AOI systems because in these cases, they fail to do what they
were programmed for. So the number of slip-throughs must be zero and - if they arise - close
investigation is necessary to prevent and eliminate them. However if this occurs repeatedly,
403
404
Materials Science - Advanced Topics
then the appropriate parts would seem to be defective. These are the pseudo-failures which
can reduce productivity so their number should as close to zero as possible. [164] ALSO
indicates some other image processing problems. The problems of AOI systems will be
described in more detail later in the chapter.
Another disadvantage is that they are usually in the ‘bottle-neck’ of the manufacturing
production line because they are not able to inspect the whole circuit board as fast as the line
can produce them. Therefore, the practice is usually to place more machines behind each other
to enable inspections to take place in parallel. Of course, this also has financial implications
which should be taken into consideration.
7. Special AOI solutions — Inspection of lead-free solder joints, flexible
substrates, wire bonding and semiconductors
According to RoHS and WEEE directives, lead-free solder alloys have to be used in commercial
electronics. This has presented a new challenge for AOI systems because of the differing optical
properties of lead-free alloy. Some solutions are shown in the following studies [166]-[173].
AOI has several further application possibilities in electronic device manufacturing e.g.
semiconductor and wire-bonding inspection. These appliances need extremely high-resolu‐
tion cameras to detect defects in the μm scale. Another interesting area is flexible substrate
inspection. Some of these special inspections are described in [174]-[179].
7.1. Differences between lead-based and lead-free solder alloys
Solders that contain lead are available with a tin content of between 5% and 70%. The compo‐
sition of the most commonly used lead solder is 63/37 Sn/Pb; this was the main type used in
electronics manufacturing until strict controls were imposed on its use for environmental
reasons. The homogeneity of the solder meniscus that formed was beneficial in that the melting
point of eutectic solder really is manifested as a single point on the phase diagram; in other
words the molten alloy solidifies at a specific temperature, rather than within a broader
temperature range. The solidified alloy can be broken down into tiny lead and tin phases of
almost 100% purity, without intermetallic layers.
In the case of non-eutectic solders, the crystallisation begins around cores of differing compo‐
sition and crystal structure, and at differing temperatures, so that during the accretion of the
individual cores the composition of the residual melt also changes. Due to this, in the case of
lead-free, non-eutectic solder alloys, certain phases solidify earlier, and these solid cores do
not form a completely mirror-like, smooth surface on the face of the solder meniscus (and
naturally, they also cause differences in the volume of the material).
Lead-free solders usually contain tin, silver and copper. Compared to lead-based solders they
have several negative properties: they are more expensive, their melting point is higher, and
they give rise to problems that do not occur when soldering with lead (the phenomenon of
whisker formation has still not been fully explored). Because their surface differs from that of
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Figure 7. Tin-lead phase diagram
lead-based solder alloys – which are much more even and mirror-like – they reflect light
differently, so different procedures may be used to verify the presence and quality of the solder
menisci.
In the case of tin-lead solders, the solidification of the melt begins around the cores that are
solid at melting point, and the individual solid phases grow at a virtually consistent rate as
the two elements separate from the melt. This is how the volumes that are rich in lead and tin
become a smooth-surfaced alloy consisting of lead and tin patches, typical of eutectic solder,
that can easily differentiated on the cross-section.
Lead-free solders do not usually form eutectic alloys, and exist in many variants with different
compositions. Tin is usually alloyed with copper and silver, but there are also alloys containing,
for example, bismuth and indium.
In the case of the non-eutectic alloys (the vast majority of lead-free alloys), however, one of the
phases begins to solidify earlier, and the alloying metal concentration of this phase will be
smaller than that of the melt. This means that the composition of the remaining part of the
alloy, which is still in a liquid state, continues to change until the eutectic composition is
405
406
Materials Science - Advanced Topics
Figure 8. Tin-copper-silver phase diagram
achieved, when it cools and solidifies. As a result of this, the microstructure that is created has
a greater surface roughness than in the previous case: as the eutectic melt ebbs away, the
intermetallic crystals that were the first to solidify create a more irregular surface. This surface
scatters light much more than the smoother, more even surface of the tin-lead solder; in other
words the proportion of diffuse reflection will be greater than that of specular reflection.
An attempt to measure the two solders with AOI equipment using the same settings will
probably result in several errors, because after the necessary image conversion procedures the
images made by the equipment will differ. For this reason, it would clearly be useful to calibrate
the AOI equipment specifically for the different solders.
In what follows we present a series of images of tin-lead eutectic and lead-free Sn-Ag-Cu solder
alloys made using a scanning electron microscope (SEM). This instrument is not suitable for
measuring the surface roughness, but it does provide an accurate, high-resolution image of
the examined surfaces and of the two solder alloys with differing composition and surface
roughness, showing the differences in height and material with spectacular contrast.
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Figure 9. Electron microscope image of the surface of a tin-lead solder meniscus
Figure 10. SEM image of the surface of a lead-based solder meniscus
407
408
Materials Science - Advanced Topics
An important question is precisely what the roughness of the pattern formed on the surface
of lead-free solder alloys depends on, and how “reliably” predictable the process of its
formation is.
Figure 11. SEM image of the surface of a lead-free solder meniscus
In the above image the two types of solder can be clearly differentiated due to the rougher
surface that is typical of the lead-free alloy. In places the surface looks quite similar to the one
assumed by the microfacet model, which simplifies reality for the purpose of mathematical
manageability; in other words, small semi-spherical formations can be observed side by side
with each other. On other parts of the picture, however, areas with no unevenness are also
visible; and we have taken the electron microscope image of an area that gives a good
illustration of a particular property of lead-free, non-eutectic solder alloys (in this case a tincopper-silver alloy), namely that due to the unevenness of the surface it reflects the light more
diffusely (in other words, it scatters the light more) than the smoother surface of a eutectic
solder. In the applied Cook-Torrance model, the roughness of the surface is described by a
single parameter, which describes the surface in average terms.
The above picture shows an SEM image of a cross-section in which the tin (light) and lead
(dark) phases of the eutectic alloy are clearly differentiated.
The above image was made at a lower magnification (400x rather than 1500x), but the phase
boundaries can still be made out, and the smooth meniscus surface typical of lead-based solder
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Figure 12. SEM image of a cross-section of a lead-based solder meniscus
Figure 13. SEM image of a cross-section of a lead-based solder meniscus
409
410
Materials Science - Advanced Topics
alloys is even more visible. In the following SEM images the rough surface typical of lead-free
solder alloys can be observed.
Figure 14. SEM image of a cross-section of a lead-free solder meniscus
The fracture in the solder meniscus seen in the above image is probably due to a contaminant,
but within the fracture is a particularly clear example of just how uneven a surface can be
formed by the lead-free solder alloy.
The surface roughness of the lead-free solder meniscus is visibly greater than that of the tinlead solder. Taking the scale bar as a guide we can also estimate that the size of the uneven
protrusions that increase the surface roughness, in terms of both their breadth and height, is
in the order of 10 μm. It is also worth noting that the simplification of the microfacet model
described by the Cook-Torrance model is clearly visible, as a visual inspection reveals that the
surface is not closely similar to the surface made up of tiny flat plates that is assumed by the
microfacet model. This simplification, however, is more than made up for by the model’s
simplicity and general ease of use.
7.2. Measuring the surface roughness
To measure the surface roughness we used a Tencor Alpha Step 500 surface profilometer. Based
on the 10 measurements of each solder, made on the lead-based (Heraeus F816 Sn63-90 B30)
and lead-free (Senju Ecosolder M705-GRN360-K1-V) joints, the two solders yielded the
following values:
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Figure 15. SEM image of a cross-section of a lead-free solder meniscus
Type of measurement result
Lead-based solder paste
Lead-free solder paste
Measured surface roughness (RMS)
0.03092
0.0986
Distribution of measured RMS value
0.004451
0.054626
Table 11. Measured surface roughness values
At first glance the measured surface roughness values appear realistic; the surface roughness
of the lead-free solder turned out to be approximately three times that of the lead-based solder.
The distribution of the roughness values for the lead-based solder was below 1%, which is
satisfactory because the divergence between the shape of the actual solder and that modelled
by the computer showed a greater error (a few percent), and because greater fluctuation than
this can be expected to result from the differing heat profiles, printed circuit boards or solderhandling requirements of real production lines. The distribution of the surface roughness
values for the lead-free solder was over 5%, which is due to diversity of the size and shape of
the surface protrusions that appear with this type of solder, which the Cook-Torrance model
handles using statistical simplification, by assuming the surface to be of a consistent roughness.
7.3. Simulation created with the computer model
We checked the measured surface roughness values by comparing the images made using
optical microscopes with the computer-generated graphic representations. The Surface
411
412
Materials Science - Advanced Topics
Evolver software uses finite element analysis to calculate the surface profile at certain points
on the surface. In areas with a greater radius of curvature, where the energies are closer to each
other in terms of magnitude (in other words none are dominant in comparison to any others)
the software uses more measurement points, that is a denser grid, for displaying the graphic
representation.
Figure 16. Example of a graphic representation generated using the Surface Evolver software
Of the models that use a formula based on the Bi-directional Reflectance Distribution Function
(BRDF), which is based on a physical approach, the most widely used is the Cook-Torrance
model, which has surface roughness as one of its input parameters and is also capable of
handling Fresnel distribution. During our simulation we used this, in a Direct3D environment,
so when generating the rendered graphics we were able to use the measured roughness values
as input parameters.
The majority of optical microscopes – including the Olympus BX51 microscope used by me at
the department – are capable of operating in bright field (BF) and dark field (DF) imaging
mode.
In bright field imaging, both the incident and reflected light fall almost perpendicularly onto
the sample, naturally through a focusing lens. Dark field microscopes, on the other hand,
collect beams of light that arrive not perpendicularly but from the side, from below a given
angle, through a lens, in the direction of the observer; in other words the beams of light travel
in the opposite direction but along the same path as would the beams of light that enter
perpendicularly but are diffracted, not reflected.
Dark field microscopy gives a good resolution and microscopes with this capability are usually
more expensive, but they are eminently suitable for the detection of phase boundaries or the
examination of surface irregularities highlighted by the side illumination. In the case of metals,
in which the proportion of diffuse components is smaller and the incident light is reflected
much more in accordance with the principle of optical reflection, bright field microscopy
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
results in a darker image and in the case of observation along the z axis (from above), as is
typical of microscopes, only the surfaces that are parallel to the horizontal are illuminated. We
also modelled both of these different types of illumination using the Direct3D software.
What follows is a comparison of the images made using the optical microscope and the graphic
representations rendered with Direct3D that most closely replicated the actual light and
surface conditions. Where not indicated separately, the soldered joint (at the SMT resistors) is
illuminated with scattered light.
Figure 17. Photograph and graphic representation of empty solder pad covered in lead-free solder (BF imaging)
Figure 18. Photograph and graphic representation of empty solder pad covered in lead-free solder (DF imaging)
413
414
Materials Science - Advanced Topics
Figure 19. Photograph and graphic representation of SMT joint made with lead-free solder
Figure 20. Photograph and graphic representation of empty solder pad covered in lead-based solder (BF imaging)
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Figure 21. Photograph and graphic representation of empty solder pad covered in lead-based solder (DF imaging)
Figure 22. Photographs (above: BF, below: DF) and graphic representation of SMT joint made with lead-based solder
415
416
Materials Science - Advanced Topics
Figure 23. Photograph and graphic representation of SMT joint made with lead-free (left) and lead-based (right) sol‐
der
8. Detailed analysis of AOI systems
As can be seen in the previous section, AOI machines handle several tasks. Much literature is
dedicated to the intelligence of these systems, but from a technical point of view we can also
examine other aspects. A large number of these AOIs work on high-mix-high-volume SMT
lines where the most important key factors are the inspection duration and quality. The
attributes of this system relate to the following sections:
1.
actuating parts (drives and axes)
2.
image acquisition system (sensors, optics, illumination)
3.
software processing part
They work in close relationship to each other, so the speed of each has to be in sync. There are
three well-defined mechanical constructions for an AOI system:
• without special moving parts / drives inside
• with PWB positioning table
• with camera-module actuating unit
The simplest case is when the working-process of the system does not include special posi‐
tioning steps. The PWB is positioned/placed in “one step” into the field of camera system, an
image is acquired and the PWB is then taken out for the next process. This could be of great
benefit because the machine does not need to synchronize any movements during the image
acquisition process. The speed affecting factor can thus be ignored. This is used typically in
Automatic Final Inspection (AFI) systems. This does not mean that the system has to only
contain one camera. For more complex applications the number of cameras can be increased.
More cameras mean more complex image transformation and manipulation tasks so it follows
that these systems are only capable of use when looking at pre-defined areas.
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
In case of larger inspection areas, the systems are mounted with special drives which can move
the camera system or the inspected part. The movements of these drives have to be synchron‐
ized with the process of illumination and image acquisition. When the system contains small
number of cameras and the illumination devices are also built-in, then the module itself should
be the moving section. When there are even more cameras, each with its separate illumination
(matrix arrangement), then the PWB should lie on a positioning table.
Two big groups of drive systems are commonly used for this purpose. The first is the conven‐
tional electromechanical drive. It is used for some 2D paste inspection machines. Here the
velocity of the camera can be constant, while in most cases it contains line-CCD sensor. The
other type of motion system is the linear drive which is more accurate and faster and therefore
in more frequent use.
The directional route of the moving part highly depends on a second factor, that of speed and
the properties of image acquisition system. Here also, three main parts can be singled out:
• optics / lenses
• camera / sensor type
• illumination module / lighting source
The system has to get the necessary amount of information and resolution from even the
smallest components. In the SMT field this means zooming down to a 10μm pixel resolution.
To ensure the constant magnification at all points of the entire Field of View (FOV) the use of
telecentric optics is essential. This criterion enables the system to make the required sizemeasurements. On an image seen through traditional lenses, the apparent shape of compo‐
nents changes with the distance from the centre of the FOV, therefore sometimes making shape
recognition a hard task.
But it is not just the permanency of magnification that is important, so too is the need to select
the correct level. On one hand, the larger detection area of the image sensor can help solve this
task, but it also increases the computational resources needed. On the other hand, higher
magnification levels give a better resolution but at the expense of reducing the field of view.
The best scenario is if the system is capable of optional magnification. Generally, a relative
large FOV, between 10-25 cm2, could be used and only in certain cases should dedicated Field
of Interests (FOI) should be zoomed out.
In most AOI applications, the LED based lighting is used for illumination purposes. But
independent of the type of illumination source used, the amount of illumination should be
only as much as is required. The optimum depends on the application. For example, a 2D paste
or a through-hole-technology (THT) components solder-joint inspection system needs only
just a small amount of illumination. As the number of failure types / inspection tasks increase
so too the number of illumination modes also increase. The programmable illumination
module is a good tool to develop lighting requirements for dedicated purposes, but it also
carries the risk of inhomogeneous and reduced FOV.
417
418
Materials Science - Advanced Topics
Ring illumination around 4 cameras
Ring illumination around 1 camera
23
11
4
-1
-3
-3
0
21
10
3
-2
-5
-5
-2
0
21
10
3
-1
-2
-2
26
15
8
4
2
3
6
34
24
16
13
11
12
15
Grey-level distribution of 1 section
45
31
21
19
21
34
47
38
22
9
6
9
22
40
36
18
4
2
5
19
37
32
16
2
-3
3
16
33
44
28
16
14
18
44
30
Grey-level distribution of 1 camera
Figure 24. Problem of inhomogeneous grey-level by ring-illumination types
Fig. 7 illustrates two types of camera-illumination systems. The first system contains 4 cameras,
the second only 1 camera. Both have LED-ring illumination modules. The grey-level distribu‐
tion maps shown above have been measured with the same type of illumination and greyreference flat. The green areas indicate the valuable field of the camera. This example clearly
points out the importance of the homogeneity. Of course this phenomenon is also present when
the illumination system is multi-coloured.
Most optical inspection / control appliance decisions are based on image-processing methods
that have been set experientially. The stress is on the word “experientially”. Most of the AOI
machines make some kind of template matching. These sample-templates can be colour or
greyscale, stand from parts/windows or form a complete pattern. The machine can be ‘selflearning or directed by means of an “external trainer”. Due to the fact that the overall reliability
of these machines is not 100%, the defined limits between good and bad classified patterns are
not strict. In some cases it could be that just two pixels differ between the data provided. If the
phenomenon which the system needs to detect is not so unambiguous, then it should search
for another method to make the gap wider between the 2 classes.
9. Software questions
One of the most wide-spread criticisms against the principles and methods of automated
optical inspection systems stems from a very interesting paradox. As we have mentioned
earlier, the introduction of AOI devices in the manufacturing lines was a result of the growth
in manufacturing process complexity. These inspection and control devices have to fulfill
certain reliability criteria which need to be validated. But unfortunately, these validation
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
processes can only be used to a limited degree because of the high-complex manufacturing
process and the equally complex and highly varied appearance of devices under test (DUTs).
This contradiction invokes the conclusion that accuracy and reliability of AOI system depends
very much on the competence and working quality of the engineers and operators, the correct
management of the setting up and controlling the inspection devices. In reality, this sets out
several very serious challenges to experts. The quality inspection algorithms have many
parameters – in some cases several hundred – (image processing, region of interest, threshold
parameters etc.). Their setup requires experience, intuition and inspiration from the process
engineers themselves.
In addition, during parameter tuning, the engineers need to solve the following contradiction,
where the difference between images showing correct and faulty components is often only a
few pixels which need to be detected by the AOI devices (Fig. 8). In the case of incorrect
parameter settings these small signals can disappear and the system classify a bad component
as good (“slip-through”). Certainly this false classification is totally intolerable in quality
inspection processes; therefore it is necessary to aim for the complete elimination of this
possibility by fine tuning the algorithm’s parameters. Unfortunately because of this, engineers
can easily set the algorithm to be too strict, meaning also that some correct components will
be dropped out during the inspection process. Although these “false calls” (also known as
pseudo failure) do not cause catastrophic consequences nevertheless they are the source of a
very serious problem. Namely, in this instance, the human operators performing the reinspection of components considered “faulty” can easily get used to the repeated mistakes of
the AOI system. Therefore they can eventually take the inspection device’s decisions out of
consideration even where there is a cases of real errors. This implies that the reliability of the
inspection device itself would be in doubt; the fact of which would result in one of the biggest
catastrophic effects on AOI systems. In addition, it seems insignificant but it is important to
note that many bad classifications slow the manufacturing process, decrease productivity and
increase the product overall production costs. To avoid false calls, process-engineers need to
reduce the strictness of the inspection parameters which – as we have mentioned earlier – is
inconsistent with principle used by the parameter settings preventing the slip-through.
Figure 25. An example for the tiny differences between the images containing correct and faulty components
419
420
Materials Science - Advanced Topics
In addition, AOI engineers need to cope with several other difficulties a major one of which is
that the production process changes continuously e.g. the settings of devices on the manufac‐
turing line have to be modified, and this needs to be followed also by modifications to the AOI
devices. Therefore the need to monitor the inspection algorithms and adapt to different
parameters is a serious challenge to the process engineers.
Furthermore, it is necessary to satisfy some practical requirements when selecting and
adjusting the inspection algorithms. Usually, electronic factories manufacture more products
in parallel in which several similar or identical components can be located. If all the compo‐
nents were to be inspected with a separate AOI algorithm, the code management, version
tracking and fixing etc. would be impossible. Therefore, engineers often use only one inspec‐
tion method for similar mountings to achieve simpler AOI algorithm version management.
Unfortunately, this strategy cannot always be used successfully because of the very heteroge‐
neous appearance of the same components. Fig.9. shows an image sequence of the C0805
capacitor which illustrates the enormous differences between images taken of similar compo‐
nents.
In this varied environment it is very hard to develop an inspection method which results in
highly reliable classification of each type of image for the same component. In addition, a
parameter setting process that reduces the number of bad classifications in case of one
component influences not only the selected manufacturing line but has an effect on the whole
factory. Therefore it can happen that whilst a parameter optimization process reduces the
number of bad classifications in the first part of the factory, it increases them on other manu‐
facturing lines. This paradox is one of the reasons why the AOI macro optimization process is
a very long and “Sisyphean” task of AOI process engineers.
Figure 26. Differences between the appearances of similar components (capacitor C0805)
A very interesting and important question is the optimization of classification thresholds. One
of the most important requirements of an inspection system is high-level robustness, but this
condition can hardly be guaranteed if the classification decision (namely whether a component
gets “faulty” or “good” label) is dependent on only one pixel. Therefore the quality results
close to the decision threshold need to be classified in a separate group (“limit error”) and it
is necessary to apply a different strategy to them. It follows that AOI experts – apart from the
fact that they need to solve the optimization paradox mentioned earlier – have to strive to find
such an algorithm parameter setting where during the classification, the number of compo‐
nents classified near the decision threshold are as few as possible. Efficiency of AOI appliances
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
can be significantly improved with the help of macro optimization. In the first task, the pseudo
rate was reduced while slip-throughs remains zero (Table VIII, Fig. 10)
Before optimization (30 days testing period)
Inspected components
(solder joints)
[pieces]
347 130
(694 260)
Detected failures [pieces]
Real failures
[pieces]
Pseudo failures
[pieces]
4 676
4
4 672
Pseudo rate
[ppm]
13 459
After optimization (30 days testing period)
Inspected components
(solder joints)
[pieces]
223 006
(446 012)
Table 12. Results of macro optimization
Figure 27. Pseudo reduction
Detected failures [pieces]
Real failures
[pieces]
Pseudo failures
[pieces]
52
2
50
Pseudo rate
[ppm]
224
421
422
Materials Science - Advanced Topics
Secondly parallel pseudo and slip-through reduction were carried out (Table IX, Fig. 11, Fig.
12).
Before optimization (30 days testing period)
Detected failures [pieces]
Inspected components
(solder joints)
[pieces]
Real failures
(Quasi-tombstone )
[pieces]
Pseudo failures
[pieces]
68 205
2 423 334
(4 846 668)
364 (0 )
67 841
Pseudo rate
[ppm]
27 995
After optimization (30 days testing period)
Inspected components
(solder joints)
[pieces]
4 655 392
(9 310 784)
Detected failures [pieces]
Real failures
(Quasi-tombstone )
[pieces]
Pseudo failures
[pieces]
58 654
627 (62 )
58 027
Pseudo rate
[ppm]
12 560
Table 13. Results of macro optimization
Another very serious question is about the parameter optimization process, namely how can
the AOI engineers validate the new parameter values determined by the optimization process?
Certainly a correction of a bad classification cannot be validated only by examination of the
specified image, but it is necessary to check several other instances. Therefore, to execute a
reliable validation process, the engineers have to collect a large image database (“image base”)
covering all cases as they occur in the best possible way. Unfortunately, creating a good and
usable image base is a long and sometimes impossible task because of several – often contra‐
dicting – criteria. A manual image collection by the engineers is very time-consuming and in
case of automatic systems (like AOIs) there is only a limited possibility because of the high
number and varied type of data. Automatic methods are faster but during the collection, some
falsely classified images can be put in the image base which makes the parameter optimization
impossible. For example, if an image containing a faulty component is placed into the “good”
part of the image base, the optimization process will try to adjust to the parameters that the
AOI algorithm has classified the image as “good”. As a result, the optimized macro cannot
recognize this specified error which can indicate slippages causing the greatest type of
inspection catastrophe.
The number of stored images is also a very important factor. If the image base contains too
many images, the resources (processor, hard drive, network etc.) become overloaded and the
optimization process can only be executed slowly. On the other hand in case of a small image
base the algorithm validation is neither reliable nor accurate enough.
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Figure 28. Pseudo and slip-through reduction
423
424
Materials Science - Advanced Topics
Figure 29. Quasi-tombstone
If we suppose that the optimal size of image base is determined (and which cannot be exceeded)
and relevant images are collected resulting in a reasonably good image base. At this point
another question arises: how can the engineers update the database with new images? It is
very hard to determine which images from the new image-set need to be stored and which
images need to be deleted from the current image base. There are several criteria – such as the
date created, the number of similar images etc. – which can be used as the basis of the updating
decision but a precise numerical factor which shows the usefulness of pictures in the database
is much more difficult to determine.
The aspects and concepts mentioned in this section have shown that the usage and perfect
operation of automated optical inspection system requires human control and supervision.
Although the devices’ algorithms are able to execute fast, accurate, efficient, reliable, “assid‐
uous” and continuous inspection (they appear to be much more suitable than human operators
as a consequence!) without being fed sufficient intelligence they cannot adapt immediately
and independently to changes in manufacturing. Therefore the quality inspection process can
hinder the increased spreading of autonomous electronic manufacture.
Several researches and developments are focusing on the problem to redeem the status of the
human operators’ work and to provide help for AOI engineers. Very interesting research
directions are in automatic algorithm parameter optimization methods. The AOI devices on
the manufacturing line monitor the quality of the algorithms (number of false calls and slipthrough, if possible) and on occasions they adjust the parameters using the image base to create
a better, higher quality algorithm. The engineers only need to take care of special cases like
changing the lighting or creating new inspection methods. Although the automatic parameter
optimization methods do not have to satisfy high real-time criteria, it is important to determine
the optimized parameter values in a relatively short time. It is easy to verify that even in the
case of having some dozen parameters; the analysis of all parameter-combinations takes a very
long time (years) therefore heuristic search methods have to be used to solve the optimization
problem.
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Certainly the automatic optimization methods also need to collect the relevant images
autonomously to create the reference image base. This work sets serious challenges for
optimization processes because of the problems and difficulties mentioned earlier.
As a summary, we can establish that AOI systems offer a powerful solution for a complex
problem by means of simple principles, but the analysis of details can reveal several problems,
difficulties and contradictions. Finding a solution for them is an essential condition for the
automated optical inspection systems in the future.
10. 3D Inspection
But the analyze-development is just one route for improving the AOI process. The other is the
“extended” optical inspection system with measuring capability. The pioneers of this property
are 3D SPI machines. In the last few years, a wide variety of these machines have been
developed. The inspection in this application - checking the SMT printing process - means the
3 dimensional measurements of solder paste bumps. These bumps are shaped like cylinders
or cubes so that the geometries and surfaces are relative simple. This fact makes the 3D optical
techniques a viable option. Several measurement techniques are used for this process, some
of these are shown in Fig. 13.
Laser triangulation
Fringe-pattern analysis
Stereovision
Shape-from-shading
Phase measuring
Moiré topography
Figure 30. optical measurement techniques
425
426
Materials Science - Advanced Topics
The same is true for inspecting component presence, but for solder-joint detection these
technologies are in their infancy at present. The shape of different components’ solder-joints
is complex and the specular surface also makes the task even more difficult. There have been
a number of research efforts, optical 3D shape measurement technologies, based on several
technologies as shown in Fig.2. Some of these researches can be found in the following studies
[181-198]. Also some companies are in the development phase such as Koh-Young Technology
[180]. So the evaluation of these geometries is as yet more difficult, but with the development
of optical metrology there will be more AOI machines with measuring capability.
11. Further developments, the future of AOI
AOI systems are following the worldwide trends i.e. multi-task integration, adaptivity, speed,
etc. There are already appliances that integrate optical inspection with repair functions: Ersa’s
AOI+R solution or optical and X-ray inspection together. Some suppliers have AOI+AXI or
Viscom’s AOXI (simultaneous inspection). Another possible area of development is the
inspection speed. Faster image capturing (with larger FOV, faster camera positioning etc),
parallel inspection of two PWBs are some possible ways for this to be done.
The other important area is adaptivity. Mainly adaptive illumination is the future of AOI
systems. It would help to drastically reduce pseudo-failures rates and eliminate slip-through
failures.
A third area is image processing. 3D inspection, neural networks, fuzzy systems, intelligent
algorithms which will help to increase the efficiency and reliability of these systems.
12. Conclusion
Inspection systems are widely used to determine the quality of electronics modules after
assembly sequences. Nowadays this is usually the automatic, non-contact and non-destructive
process, such as automatic optical inspection (AOI), supplemented with automatic X-ray
inspection (AXI) if necessary. These appliances inspect the ready or the incomplete printed
wiring boards to determine the quality of it's given property in any technological sequence,
such as paste printing, component placement or soldering. The rapid development of elec‐
tronics module assembly manufacturing requiring parallel development of test procedures.
The automatic optical inspection is potential multi-disciplinary research area, because from
image acquiring, (illumination, the detection of the reflected light etc.) through image proc‐
essing, to the evaluation each area can be optimized to reach to goal, that the qualification of
the inspected object in the field of interest (FOI) by the used appliance, matches the specifica‐
tions as stated. Most manufacturers agree that, from a strategic point of view, the optical
inspection after soldering should not be ignored. As a consequence, this is the most important
part of an AOI inspection. The quality of solder joints is determined from geometric and optical
properties of the solder meniscus. These parameters determine the reflection properties of the
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
meniscus. The meniscus forms from the liquid alloy during the soldering process. After
cooling, the meniscus becomes solid and reflects illumination which means that we can classify
them. From these reflection patterns and with the help of image processing algorithms we are
able to determine the quality of the solder joints. As described above, the correct source of
illumination is essential. There are several different kinds of approach: white or RGB; directed
or diffuse; ring or hemisphere.
This survey gives state of the art review of current automated optical inspection systems in
the electronic device manufacturing industry. The aim of the chapter is to give an overview
about the development phases, operating mechanisms, advantages and disadvantages of AOI
appliances, their technical parameters, field of usage, capabilities and possible trends for
further developments.
Author details
Mihály Janóczki1*, Ákos Becker2, László Jakab3, Richárd Gróf4 and Tibor Takács5
*Address all correspondence to: mihaly.janoczki@eu.agc.com
1 AGC Glass Hungary Ltd., H-2851 Környe, Hungary
2 DENSION Audio Systems Ltd., Budapest, Hungary
3 Department of Electronics Technology, Budapest University of Technology and Econom‐
ics, Budapest, Hungary
4 Epcos AG, Heidenheim, Deutschland
5 Department of Control Engineering and Information Technology, Budapest University of
Technology and Economics, Budapest, Hungary
References
[1] Matthew T. Holzmann, Automatic Optical Inspection Of Circuit Assemblies In a
High Mix/Low Volume Environment, Christopher Associates, Inc. Santa Ana, Cali‐
fornia USA
[2] Miran Burmen, Franjo Pernuš and Boštjan Likar, LED Light Sources: A Survey of
Quality-Affecting Factors and Methods for Their Assessment, Measurement and Sci‐
ence Technology Vol. 19, No.12, 2008, 122002 (15pp)
427
428
Materials Science - Advanced Topics
[3] Sheng-Lin Lu, Xian-Min Zhang, Yong-Cong Kuang, Optimized Design of an AOI Il‐
luminator, Proceedings of the 2007 International Conference on Wavelet Analysis
and Pattern Recognition, Beijing, China, 2-4 Nov. 2007, pp. 924-928
[4] Yuji Takagi, Seiji Hata, Susumu Hibi, Visual Inspection Machine for Solder Joints Us‐
ing Tiered Illumination, SPIE Machine Vision Systems Integration in Industry, Vol.
1386, 1990, pp. 21-29
[5] Y.J. Roh, D.Y. Lee, M.Y.Kim, H.S. Cho, A Visual Inspection System with Flexible Illu‐
mination and Auto-focusing, Proceedings of SPIE, Vol. 4902, 2002, pp. 463-475
[6] Alexander Hornberg, Handbook of Machine Vision, Wiley-VCH Verlag GmbH Co
KGaA, Weinheim, 2006
[7] E.R.Davies, Machine Vision: Theory, Algorithms, Practicalities, Elsevier, 2005
[8] Kjell J. Gasvik, Optical Metrology 3rd Edition, John Wiley & Sons Ltd., ISBN:
0-470-84300-4, 2002
[9] Shree K. Nayar, Arthur C. Sanderson, Lee E. Weiss, David A. Simon, Specular Sur‐
face Inspection Using Structured Highlight and Gaussian Images, IEEE Transaction
on Robotics and Automation, Vol. 6, No. 2, April 1990
[10] Peter Conlon, AOI A Strategy for Closing the Loop, Surface Mount Technology,
April 2006, pp. 24-29
[11] Pamela R. Lipson, Imagen and Landrex Technologies, AOI Systems Simulate Human
Brain, Test & Measurement World, February 2007, pp. 35-42
[12] Herbert Tietze, Jens Kokott (GOEPEL electronic GmbH), Application of AOI Systems
in Backplane Manufacturing
[13] Titus T. Suck (Orbotech), Controlling the Process: Post-Reflow AOI (Automated Op‐
tical Inspection) to Ascertain Machine and Process Capability
[14] Don Miller (YesTech), Exploring AOI and X-ray http://www.dataweek.co.za/
news.aspx?pklNewsId=31727&pklCategoryID=49, (accessed 12 July 2009)
[15] Mark J. Norris, Advances in Automatic Optical Inspection, Gray Scale Correlation vs.
Vectoral Imaging, Journal of Surface Mount Technology, Vol. 15, January 2002
[16] Christopher C. Yang, Michael M. Marefat, Frank W. Ciarallo, Error Analysis and
Planning Accuracy for Dimensional Measurement in Active Vision Inspection, IEEE
Transactions on Robotics and Automation, Vol. 14, No. 3, June 1998
[17] Jens Kokott, The capability of modern AOI systems, Global SMT & Packaging, No‐
vember/December 2006, pp. 16-17
[18] Matthew Holzmann, AOI in a High-Mix/Low-Volume Environment, Circuits Assem‐
bly, June 2004, pp. 30-35
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
[19] Keith Fairchild, Evaluating ROI of AXI vs. AOI, Circuits Assembly, October 2006, pp.
20-25
[20] “Realising Expectations of AOI http://www.dataweek.co.za/news.aspx?pklNew‐
sId=30534&pklCategoryID=4914, (accessed 03 July 2009)
[21] David Doyle, Challenges for Second Generation Automated Optical Inspection (AOI)
Solutions
[22] K C Fan, C Hsu, Strategic Planning of Developing Automatic Optical Inspection
(AOI) Technologies in Taiwan, 7th International Symposium on Measurement Tech‐
nology and Intelligent Instruments, Journal of Physics, Conference Series 13, 2005,
pp. 394-397
[23] Peter Krippner, T&M, Zero-defect IC Inspection Strategy With AOI, 2006 Electronics
Manufacturing,
March
2006,
http://www.emasiamag.com/currentIssue.asp?
d=1&m=3&y=2006# (accessed 12 July 2009)
[24] Mukul Shirvaikar, Trends in Automated Visual Inspection, Real-Time Image Proc‐
essing, Springer, 2006, 1:41–43
[25] Bob Ries, New Advances in AOI Technologies, Surface Mount Technology, January
2001, pp. 62-66
[26] Duncan Nicol, The Key (or Start Button) to Success, EPP Europe, November 2008, pp.
47-49
[27] Douglas W. Raymond, Dominic F. Haigh, Why Automated Optical Inspection, Inter‐
national Test Conference, 1997, pp. 1033
[28] Pamela Lipson, To Be or Not to Be in Color:A 10 Year Study of the Benefits and Pit‐
falls of Including Color Information in AOI Systems, Proceedings of the IPC APEX
Technical Conference EXPO, 2009
[29] Pamela Lipson, Lyle Sherwood, The landscape of PCB Technology is Changing Rap‐
idly. How Will AOI Testing Keep Up?, Proceedings of the IPC APEX Technical Con‐
ference EXPO, 2009
[30] Teledyne,DALSA,http://www.teledynedalsa.com/public/dc/documents/
Image_Sensor_Architecture_Whitepaper_Digital_Cinema_00218-00_03-70.pdf
cessed 12 July 2009)
(ac‐
[31] Teledyne DALSA Applications Set Imager Choices, Dalsa Aplication Note, http://
www.dalsa.com/public/corp/applications_set_imager_choices.pdf (accessed 12 July
2009)
[32] Dave Litwiller, CMOS vs. CCD: Maturing Techologies, Maturing Markets, Reprint
from the 2005 August issue of Photonics Spectra, Laurin Publishing
429
430
Materials Science - Advanced Topics
[33] Stuart A. Taylor, CCD and CMOS Imaging Array Technologies: Technology Review,
Technical Report EPC-1998-106, Xerox Research Centre Europe http://
www.research.microsoft.com/pubs/80353/CCD.pdf (accessed 12 July 2009)
[34] Sharma, A Look at CCD Sensors…, What Digital Camera Magazine; November 1997,
pp. 54-56
[35] News Item, CMOS to Signal end of Line for CCD?, What Digital Camera Magazine;
June 1997
[36] Dunn, James F.; “A New Digital Camera Startup Busts Price/Performance Standards
with CMOS Sensor, Advanced Imaging; January 1997
[37] Young Y. Cha, J.H. Oh, A Field-of-View Generation Algorithm Using Neural Net‐
work, Mechatronics Vol. 11, 2001, pp. 731-744
[38] http://en.wikipedia.org/wiki/Barcode#Matrix_.282D.29_barcodes (accessed 12 July
2009)
[39] Ja H. Koo, Suk I. Yoo, A Structural Matching for Two-Dimensional Visual Pattern In‐
spection, IEEE International Conference on Systems, Man and Cybernetics, Vol. 5,
1998, pp. 4429-4434
[40] Wen-Yen Wu, Mao-Jim J. Wang, Chih-Ming Liu, Automated Inspection of Printed
Circuit Boards Through machine Vision, Computers in Industry, Vol. 28, 1996, pp.
103-111
[41] H. Rau, C.-H.Wu, Automatic Optical Inspection for Detecting Defects on Printed Cir‐
cuit Board Inner Layers, The International Journal of Advanced Manufacturing Tech‐
nology, Vol. 25, 2005, pp. 940-946
[42] Zuwairie Ibrahim, Zulfakar Aspar, Syed Abdul Rahman Al-Attas, Musa Mohd Mok‐
ji, Corse Resolution Defect Localization Algorithm for an Automated Visual Printed
Circuit Board Inspection, 28th Annual Conference of the IECON 02, Vol. 4, 2002, pp.
2629-2634
[43] Fikret Ercal, Filiz Bunyak, Hao Feng, Context-Sensitive Filtering in RLE for PCB In‐
spection, Conference of Intelligent Systems in Design and Manufacturing, Vol. 3517,
1998, pp. 286-293
[44] M Moganti, F Ercal, CH Dagli, S Tsunekawa, Automatic PCB Inspection Algorithms:
A Survey, Computer Vision and Image Understanding, Vol. 63, No. 2, March 1996,
pp. 287-313
[45] Péter Szolgay, Katalin Tömördi, Optical Detection of Breaks and Short Circuits on
the Layouts of Printed Circuit Boards Using CNN, Cellular Neural Networks and
their Applications, Fourth IEEE International Workshop, 24-26 Jun. 1996, pp 87-92,
DOI: 10.1109/CNNA.1996.566498
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
[46] Ji-joong Hong, Kyung-ja Park, Kyung-gu Kim, Parallel Processing Machine Vision
System for Bare PCB Inspection, IEEE, 1998, pp. 1346-1350
[47] Madhav Moganti, Fikret Ercal, Cihan H. Dagli, Shou Tsunekawa, Automatic PCB In‐
spection Algorithms: A Survey, Computer Vision and Image Understanding, Vol. 63,
No. 2, March, 1996, pp. 287-313
[48] Timót Hidvégi, Péter Szolgay, Some New Analogic CNN Algorithms for PCB Quali‐
ty Control, International Journal of Circuit Theory and Applications Archive, Vol. 30,
Issue 2-3, March 2002, pp. 231-245
[49] Fabiana R. Leta, Flávio F. Feliciano, Flavius P. R. Martins, Computer Vision System
for Printed Circuit Board Inspection, ABCM Symposium Series in Mechatronics, Vol.
3, 2008, pp.623-632
[50] Noor Khafifah Khalid, Zuwairie Ibrahim, and Mohamad Sshukri Zainal Abidin, An
Algorithm To Group Defects On Printed Circuit Board For Automated Visual Inspec‐
tion, IJSSST, Vol. 9, No. 2, May 2008, pp. 1-10
[51] Nam Hyeong Kim, Jae Young Pyun, Kang Sun Choi, Byeong Doo, Choi, SungJea Ko,
Real-Time Inspection System for Printed Circuit Boards, Lecture Notes in Computer
Science, Vol. 2781, 2003, pp. 458-465
[52] Amistar Automation Inc.: K5/K5L Desktop Board Inspector, Data Sheet, Updated
November, 2007
[53] Lloyd Doyle Limited duotech, Data Sheet, Updated Septembe, 2005
[54] Lloyd Doyle Limited Excalibur 5000 X2, Data Sheet, Updated September, 2005
[55] Lloyd Doyle Limited LD 6000, Data Sheet
[56] Lloyd Doyle Limited phasor, Data Sheet, Updated September, 2005
[57] Lloyd Doyle Limited redline, Data Sheet, Updated September, 2005
[58] I. Fidan, R. P. Kraft, L. E. Ruff, S. J. Derby, Designed Experiments to Investigate the
Solder Joint Quality Output of a Prototype Automated Surface Mount Replacement
System, IEEE Transactions on Components, Packaging, Manufacturing Technology,
Vol. 21, No. 3, Jul. 1998, pp. 172-181
[59] J. Pan, G. L. Tonkay, R. H. Storer, R. M. Sallade, D. J. Leandri, Critical Variables of
Solder Paste Stencil Printing for Micro-BGA and Fine Pitch QFP, Proceedings on the
24th IEEE/CPMT International Electronics Manufacturing Technology Symposium,
1999, pp. 94-101
[60] D. He, N. N. Ekere, M. A. Currie, The Behavior of Solder Pastes in Stencil Printing
With Vibrating Squeegee, IEEE Transactions on Components, Packaging, Manufac‐
turing Technology, Vol. 21, No. 4, Oct. 1998, pp. 317-324
431
432
Materials Science - Advanced Topics
[61] S. C. Richard, The Complete Solder Paste Printing Processes, Surface Mount Technol‐
ogy, Vol. 13, 1999, pp. 6-8
[62] Peter Krippner, Detlef Beer, AOI Testing Position in Comparison, Circuits Assembly,
April 2004, pp. 26-32
[63] David P. Prince, Bridge Detection in the Solder Paste Print Process, 1st January 2006,
http://www.speedlinetech.com/docs/Bridge-Detection-Solder-Paste.pdf (accessed 12
July 2009)
[64] K. Fauber, S. Johnson, 2D Versus 3D Solder
www.agilent.com/see/aoi 2003 (accessed 12 July 2009)
Paste
Inspection
http://
[65] Rita Mohanty, Vatsal Shah, Paul Haugen, Laura Holte, Solder Paste Inspection Tech‐
nologies: 2D-3D Correlation, Proceedings of the APEX Conference, April 2008
[66] R. R. J. Lathrop, Solder Paste Print Qualification Using Laser Triangulation, IEEE
Transactions on Components, Packaging, Manufacturing Technology, Vol. 20, 1997,
pp.174-182
[67] Okura, M. Kanai, S. Ogata, T. Takei, H. Takakusagi, Optimization of Solder Paste
Printability With Laser Inspection Technique, Proceedings ot the IEEE/CPMT Inter‐
national Electronics Manufacturing Technology Symposium, 1997, pp. 361-365
[68] E. H. Rideout, Lowering Test Costs With 3-D Solder-Joint Inspection, Test Measure‐
ment World, Vol. 10, 1990, pp. 744-1657
[69] Y. K. Ryu, H. S. Cho, New Optical Measuring System for Solder Joint Inspection, Op‐
tics and Lasers in Engineering, Vol. 26, No. 6, April 1997, pp. 487-514
[70] H. Tsukahara, Y. Nishiyama, F. Takahashi, T. Fuse, M. Ando, T. Nishino, High-Speed
3-D Inspection System For Solder Bumps, Proceedings of SPIE, Vol. 2597, 1995, pp.
168-177
[71] J. L. Horijon, W. D. Amstel, F. C. Couweleer, W. C. Ligthart, Optical System of an In‐
dustrial 3-D Laser Scanner for Solder Paste Inspection, Proceedings of SPIE, Vol.
2599, 1995, pp. 162-170
[72] T. Xian, X. Su, Area Modulation Grating for Sinusoidal Structure Illumination on
Phase-Measuring Profilometry, Applied Optics, Vol. 40, 2001, pp.1201-1206
[73] Hsu-Nan Yen, Du-Ming Tsai, Jun-Yi Yang, Full-Field 3-D Measurement of Solder
Pastes Using LCD-Based Phase Shifting Techniques, IEEE Transactions on Electron‐
ics Packaging, Manufacturing, Vol. 29, No. 1, January 2006, pp. 50-57
[74] XinYu Wu, WingKwong Chung, Hang Tong, A New Solder Paste Inspection Device:
Design and Algorithm, IEEE International Conference on Robotics and Automation.
Roma, Italy, 10-14 April 2007, pp. 680-685
[75] Fang-Chung Yang, Chung-Hsien Kulo, Jein-Jong Wing, Ching-Kun Yang, Recon‐
structing the 3D Solder Paste Surface Model Using Image Processing and Artificial
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
Neural Network, IEEE International Conference on Systems, Man and Cybernetics,
Vol. 3, 2004, pp. 3051-3056
[76] Feng Zhang, AEWMA Control Chart for Monitoring Variability Sources of Solder
Joints Quality, IEEE Transactions on Components and Packaging Technologies, Vol.
29, No. 1, March 2006, pp. 80-88
[77] Xinyu Wu, Wing Kwong Chung, Jun Cheng, Hang Tong, Yangsheng Xu, A ParallelStructure Solder Paste Inspection System, IEEE/ASME Transactions on Mechatronics,
Vol. 14, No. 5, October 2009, pp. 590-597
[78] CyberOptics SE 300 Ultra, Data Sheet, 8009067, Rev E 1/08
[79] CyberOptics SE 500 Solder Paste Inspection System, Data Sheet, 8013863, Rev A 3/09
[80] Koh Young Technology aSPIre, Data Sheet, aSPIre-B-08-2008-E
[81] Koh Young Technology KY-8030 Series, Data Sheet, KY-8030-B-08-2008-E
[82] Marantz Power Spector SPI Series, 5D Solder Paste Inspection System, Data Sheet,
Printed October, 2009, Updated November, 2009
[83] Omron VT-RNS-P, VT-RNS Series, Automated Optical Inspection System, Data
Sheet, Printed in Japan, 0807-5M (1004) (D)
[84] Omron CKD VP5000L, Solder Printing Inspection Machine, Data Sheet, Printed July,
2007
[85] Saki BF-SPIder, 3D In-Line Solder Paste Inspection System, Data Sheet, Printed Au‐
gust 2009, PM01DCF01-08.5E
[86] TRI Innovation TR7006 Series, 3D Solder Paste Inspection System, Data Sheet, Updat‐
ed November, 2008, C-7006-EN-0810
[87] ScanCAD ScanINSPECT SPI, Solder Paste Inspection, Data Sheet, Updated January,
2007
[88] TRI Innovation TR7066 Series, 3D Solder Paste Inspection System, Data Sheet, Updat‐
ed November, 2009, C-7066-EN-0909
[89] Viscom S3088-II QS, Data Sheet, Updated February, 2009, Viscom_SYS_S3088-II
QS_EN09020001
[90] ViTechnology 3D-SPI, Data Sheet, 3D-SPI EN Rev 0507-2009
[91] Michael E. Zervakis, Stefanos K. Goumas, George A. Rovithakis, A Bayesian Frame‐
work for Multilead SMD Post-Placement Quality Inspection, IEEE Transactions on
Systems, Man and Cybernetics - Part B, Vol. 34, No. 1, February 2004, pp. 440-453
[92] Gao Hongxia, Hu Yueming, Liu Haiming, Fang Xiaosheng, A Fast Method for De‐
tecting and Locating BGA Based on Twice Grading and Linking Technique, 22nd
433
434
Materials Science - Advanced Topics
IEEE International Symposium on Intelligent Control Part of IEEE Multi-conference
on Systems and Control Singapore, 1-3 October 2007, pp. 375-378
[93] Bernard C. Jiang, Szu-Lang Tasi, Chien-Chih Wang, Machine Vision-Based Gray Re‐
lational Theory Applied to IC Marking Inspection, IEEE Transactions on Semicon‐
ductor Manufacturing, Vol. 15, No. 4, November 2002
[94] John Weisgerber, Doreen Tan, Pre-Reflow, Inline, 3-D Inspection, Circuits Assembly,
November 2003, pp. 34-36
[95] Chun-Ho Wu, Da-Zhi Wang, Andrew Ip, Ding-Wei Wang, Ching-Yuen Chan, HongFeng Wan, A Particle Swarm Optimization Approach for Components Placement In‐
spection on Printed Circuit Boards, Journal of Intelligent Manufacturing, 2008, DOI
10.1007/s10845-008-0140-2
[96] L. Shih-Chieh, C. Chih-Hsien, S. Chia-Hsin, A Development of Visual Inspection Sys‐
tem for Surface Mounted Devices on Printed Circuit Board, Proceedings of the 33rd
Annual Conference of the IEEE Industrial Electronics Society (IECON), Taipei, Tai‐
wan, 2007, pp. 2440-2445
[97] A. J. Crispin, V. Rankov, Automated Inspection of PCB Components Using a Genetic
Algorithm Template-Matching Approach, The International Journal of Advanced
Manufacturing Technology, Vol. 35, No. 3-4, December 2007, pp. 293-300
[98] E. Guerra, J.R.Villalobos, A Three-Dimensional Automated Visual Inspection System
for SMT Assembly, Computer and Industrial Engineering, Vol. 40, 2001, pp. 175-190
[99] BeamWorks Inspector cpv, An In-Line AOI System from BeamWorks, Data Sheet,
Updated April, 2002
[100] Landrex Optima II 7301 Express, Data Sheet, Updated July, 2006
[101] Omron VT-RNS-Z, VT-RNS Series, Automated Optical Inspection System, Data
Sheet, Printed in Japan, 0807-5M (1004) (D)
[102] Viscom S3054QV, Data Sheet, Updated May, 2006
[103] Hongwei Xie, Yongcong Kuang, Xianmin Zhang, A High Speed AOI Algorithm for
Chip Component Based on Image Difference, International Conference on Informa‐
tion and Automation, Zhuhai/Macau, China, 22-25 June, 2009, pp. 969-974
[104] G. Acciani, G. Brunetti, G. Fornarelli, A Multiple Neural Network System to Classify
Solder Joints on Integrated Circuits, International Journal of Computational Intelli‐
gence Research, Vol. 2, No. 4, 2006, pp. 337-348
[105] Y. K. Ryu, H. S. Cho, A Neural Network Approach to Extended Gaussian Image
Based Solder Joint Inspection, Mechatronics, Vol. 7, No. 2, 1997, pp. 159-184
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
[106] Fan-Hui Kong, A New Method for Locating Solder Joint Based On Rough Set, Pro‐
ceedings of the Sixth International Conference on Machine Learning and Cybernet‐
ics, Hong Kong, 19-22 August 2007, pp. 3678-3681
[107] Fan-Hui Kong, A New Method of Inspection Based on Shape from Shading, Con‐
gress on Image and Signal Processing, 2008, pp. 291-294
[108] Giuseppe Acciani, Gioacchino Brunetti, Girolamo Fornarelli, Application of Neural
Networks in Optical Inspection and Classification of Solder Joints, Surface Mount
Technology, IEEE Transasctions on Industrial Informatics, Vol. 2, No. 3, 2006, pp.
200-209
[109] N.S.S. Mar, C. Fookes, P.K.D.V. Yarlagadda, Design of Automatic Vision-Based In‐
spection System for Solder Joint Segmentation, Journal of Achievements in Materials
and Manufacturing Engineering Vol. 34. Issue 2, 2009, pp. 145-151
[110] Da-Zhi Wang, Chun-Ho Wu, Andrew Ip, Ching-Yuen Chan, Ding-Wei Wang, Fast
Multi-Template Matching Using a Particle Swarm Optimization Algorithm for PCB
Inspection, Lecture Notes in Computer, No. 4974, 2008, pp. 365-370
[111] Hugo C. Garcia, J. René Villalobos, George C. Runger, An Automated Feature Selec‐
tion Method for Visual Inspection Systems, IEEE Transactions on Automation Sci‐
ence and Engineering, Vol. 3, No. 4, 2006, pp. 394-406
[112] Y. K. Ryu, H. S. Cho, New Optical Measuring System for Solder Joint Inspection, Op‐
tics and Lasers in Engineering, Vol. 26, 1997, pp. 487-514
[113] Huibin Zhao, Jun Cheng Jianxun Jin, NI Vision Based Automatic Optical Inspection
(AOI) for Surface Mount Devices: Devices and Method, Proceedings of 2009 IEEE In‐
ternational Conference on Applied Superconductivity and Electromagnetic Devices
Chengdu, China, 25-27 September 2009, pp. 356-360
[114] Horng-Hai Loh, Ming-Sing Lu, Printed Circuit Board Inspection Using Image Analy‐
sis, IEEE Transactions on Industry Applications, Vol. 35, No. 2, March/April 1999,
pp. 673-677
[115] Shao-Nung Chiu, Ming-Hwei Perng, Reflection-Area-Based Feature Descriptor for
Solder Joint Inspection, Machine Vision and Applications, Vol. 18, 2007, pp. 95–106
[116] Li Ni, Pan Kai-Lin, Li Peng, Research on Solder Joint Intelligent Optical Inspection
Analysis, 2008 International Conference on Electronic Packaging Technology & High
Density Packaging (ICEPT-HDP), 28-31 July 2008, pp. 1-4
[117] Kuk Won Ko, Hyung Suck Cho, Solder Joints Inspection Using a Neural Network
and Fuzzy Rule-Based Classification Method, IEEE Transactions On Electronics
Packaging Manufacturing, Vol. 23, No. 2, April 2000, pp. 93-103
435
436
Materials Science - Advanced Topics
[118] Andy Yates, Steven Brown, Jim Hauss, Paul Hudec, Inspection Strategies for 0201
Components, CyberOptics Corporation 2002, First Published: SMTA International,
Chicago 2002
[119] T. Y. Ong, Z. Samad, M. M. Ratnam, Solder Joint Inspection With Multi-Angle Imag‐
ing and an Artificial Neural Network, The International Journal of Advanced Manu‐
facturing Technology, Vol. 38, No. 5-6, August 2008, pp. 455-462
[120] Y. Ousten, S. Mejdi, A. Fenech, J.Y. Deletage, L. Bechou, M.G. Perichaud, Y. Danto,
The Use of Impedance Spectroscopy, SEM and SAM Imaging for Early Detection of
Failure in SMT Assemblies, Microelectronics Reliability, Vol. 38, Issue 10, October
1998, pp. 1539-1545
[121] T.H.Kim, T.H.Cho, Y.S.Moon, S.H.Park, Visual Inspection System for the Classifica‐
tion of Solder Joints, Pattern Recognition, Vol. 32, No. 4, 1999, pp. 565-575
[122] T. Hiroi, K. Yoshimura, T. Ninomiya, T. Hamada, Y. Nakagawa, Development of Sol‐
der Joint Inspection Method Using Air Stimulation Speckle Vibration Detection
Method and Fluorescence Detection Method, IAPR Workshop on Machine Vision
Applications, Tokyo, 7-9 December 1992, pp. 429-434
[123] R. Vanzetti, A. C. Traub, A. A. Richard, Automated Laser Inspection of Solder Joints,
ISTFA, 1981, pp.85-96
[124] Z.S. Lee, R.C. Lo, Application of Vision Image Cooperated With Multi-Light Sources
o Recognition of Solder Joints For PCB, TAAI, Artificial Intelligence and Applica‐
tions, 2002, pp. 425-430
[125] B. C. Jiang, C.C. Wang, Y.N. Hsu, Machine Vision and Background Remover-Based
Approach for PCB Solder Joints Inspection, International Journal of Production Re‐
search, Vol. 45, No. 2, 2007, pp. 451-464
[126] J.H. Kim, H.S. Cho, S. Kim, Pattern Classification of Solder Joint Images Using a Cor‐
relation Neural Network, Engineering Applications of Artificial Intelligence, Vol. 9,
No. 6, 1996, pp. 655-669
[127] T.H. Kim, T.H. Cho, Y.S. Moon, S.H. Park, Visual Inspection System For The Classifi‐
cation of Solder Joints, Pattern Recognition Vol. 324, 1999, pp. 565-575
[128] J.H. Kim, H.S. Cho, Neural Network-Based Inspection of Solder Joints Using a Circu‐
lar Illumination, Image and Vision Computing, Vol. 13, No. 6, 1995, pp. 479-490
[129] K.W. Ko, H.S. Cho, Solder Joints Inspection Using a Neural Network and Fuzzy
Rule-Based Classification Method, IEEE Transactions on Electronics Packaging Man‐
ufacturing, Vol. 23, No. 2, 2000, pp. 93-103
[130] T.S. Yun, K.J. Sim, H.J. Kim, Support Vector Machine-Based Inspection of Solder
Joints Using Circular Illumination, Electronics Letters Vol. 36, No. 11, 2000, pp.
949-951
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
[131] D.W. Capson, S.K. Eng, A Tiered-Color Illumination Approach for Machine Inspec‐
tion of Solder Joints, IEEE Transactions on Pattern Analysis and Machine Intelligence
Vo. 10, No. 3, 1988, pp. 387-393
[132] J. H. Kim, H. S. Cho, S. Kim, Pattern Classification of Solder Joint Images Using a
Correlation Neural Network, Engineering Applications of Artificial Intelligence, Vol.
9, No. 6, 1996, pp. 655-669
[133] Tae-Hyeon Kim, Tai-Hoon Cho, Young Shik Moon, Sung Han Park, Visual Inspec‐
tion System for the Classification of Solder Joints, Pattern Recognition Vol. 32, 1999,
pp. 565-575
[134] TRI Innovation 7530 Series, Automated Optical Inspection System, Data Sheet, Up‐
dated September, 2009, C-7530-EN-0909
[135] Viscom S3016, Data Sheet, Updated Marc, 2007, VISCOM_SYS_S3016_EN06100001
[136] Viscom
S3054QC,
Data
COM_SYS_S3054QC_EN06050002
Sheet,
Updated
June,
2007,
VIS‐
[137] S. Jagannathan, Automatic Inspection of Wave Soldered Joints Using Neural Net‐
works, Journal of Manufacturing Systems, Vol. 16, Issue 6, 1997, pp. 389-398
[138] K. Sundaraj, Homogeneous Pin-Through-Hole Component Inspection Using Fringe
Tracking, WSEAS Transactions on Signal Processing, Vol. 4, Issue 7, July 2008, pp.
419-429
[139] F. Wu, X. Zhang, Y. Kuan, Z. Z. He, An AOI Algorithm for PCB Based on Feature
Extraction Inspection, Proceedings of the 7th World Congress on Intelligent Control
and Automation, Chongqing, China, 25-27 June 2008, pp. 240-247
[140] Machine Vision Products AutoInspector Series - Supra E, Data Sheet, Updated May,
2009
[141] Marantz iSpector HDL Series, In-Line Automatic Optical Inspection Systems, Data
Sheet, Printed September, 2009
[142] Marantz iSpector HML Series, In-Line Automatic Optical Inspection Systems, Data
Sheet, Printed September, 2009
[143] Saki BF-Tristar, Simultaneous High Speed Inspection for Both Sides of PCB Automat‐
ed Optical Inspection System, Data Sheet, Printed August, 2009, SJ08DCF01-02.5E
[144] Sony SI-V200, PWB Visual Inspection Machine, Data Sheet, Updated May, 2007,
043E-0705-05-01
[145] TRI Innovation TR7500 Series, Automated Optical Inspection System, Data Sheet,
Updated May, 2009, C-7500-EN-0905
[146] TRI Innovation TR7550 Series, Automated Optical Inspection System, Data Sheet,
Updated October, 2009, C-7550-EN-0910
437
438
Materials Science - Advanced Topics
[147] Agilent Medalist SJ50 Series 3, Automated Optical Inspection (AOI) and Measure‐
ment, Data Sheet, Printed in the USA September, 2006, Updated December 26, 2006
5989-5518EN
[148] Agilent Medalist sj5000, Automated Optical Inspection Solution, Data Sheet, Printed
in the USA April, 2008, 5989-7547EN
[149] Amistar Auomation Inc. K2/K2L Optical Circuit Card Inspector, Data Sheet, Updated
November, 2008
[150] CyberOptics Flex HR AOI System, Data Sheet, 8013862, Rev A 3/09
[151] Machine Vision Products AutoInspector Series – Ultra IV, Data Sheet, Updated Marc,
2009
[152] Omron VT-RNS-S, VT-RNS Series, Automated Optical Inspection System, Data
Sheet, Printed in Japan, 0807-5M (1004) (D)
[153] Omron VT-WIN II, Printed Circuit Board Inspection System, Data Sheet, Printed in
the USA 2002
[154] Saki BF-Frontier, Inline High Resolution, High Speed Automated Optical Inspection
System, Data Sheet, Printed August, 2009, SJ16DCF01-02.5E
[155] Saki BF-Planet-X, Inline High Resolution, High Speed Automated Optical Inspection
System, Data Sheet, Printed August, 2009, SJ06DCF01-02.5E
[156] Viscom S3088-III, Data Sheet, Updated October, 2009, Viscom_SYS_S3088III_EN09100001
[157] Viscom S3088-II,
II_EN08080003
Data
Sheet,
Updated
August,
2008,
Viscom_SYS_S3088-
[158] Viscom S6056, Data Sheet, Updated July, 2009, Viscom_SYS_S6056_EN09070007
[159] ViTechnology 3K Series, AOI/AOM Systems, Data Sheet, 3K Series EN Rev 04
07-2009
[160] ViTechnology 5K Series, AOI/AOM Systems, Data Sheet, 5K Series EN Rev 03
08-2009
[161] ViTechnology 7K Series, AOI/AOM Systems, Data Sheet, 7K Series CH Rev 02
08-2009
[162] ViTechnology Vi-5000 Series, AOM Systems, Data Sheet, Vi-5000 Series EN Rev 03
01-2008
[163] YES Tech YTV F1 Series, Automated PCB Inspection, Data Sheet, Updated Novem‐
ber, 2005
[164] YES Tech YTV M1 Series, Automated PCB Inspection, Data Sheet, Updated Marc,
2009
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
[165] Egidijus Paliulis, Raimondas Zemblys, Gintautas Daunys, Image Analysis Problems
in AOI Systems, Information Technology and Control, Vol. 37, No. 3, 2008, pp.
220-226
[166] Stig Oresjo, Thorsten Niermeyer, Stacy Johnson, Putting Pb-Free to the Test, Circuits
Assembly, May. 2005, pp. 42
[167] Detlef Beer, AOI In The Lead-Free Age, OnBoard Technology, June 2005, pp. 36-38
[168] Detlef Beer, Lead-Free: AOI in High-Volume Production Assemblies, Surface Mount
Technology, January 2006, pp. 40-41
[169] Thorsten Niermeyer, Controlling Pb-Free Processes Through AOI, Circuits Assem‐
bly, October 2004, pp. 40-41
[170] Paul R. Groome, Lead-Free, PCB Test and Inspection, Surface Mount Technology,
November 2005, pp. 36-39
[171] Shu Peng, Sam Wong TS, Test Implications of Lead-Free Implementation in a HighVolume Manufacturing Environment, Proceedings of the International Test Confer‐
ence (ITC), 8-8 November 2005, pp. 620-627
[172] Paul R. Groome, Lead-Free: PCB Test and Inspection, Surface Mount Technology, 1
November 2005, pp. 36-39
[173] Jim Fishburn, Advances in Lead-Free Soldering and Automatic Inspection, Surface
Mount Technology, November 2002, pp. 46-47
[174] Du-Ming Tsai, Cheng-Hsiang Yang, A Quantile–Quantile Plot Based Pattern Match‐
ing for Defect Detection, Pattern Recognition Letters, No. 26, 2005, pp. 1948-1962
[175] Ahmed Nabil Belbachir, Mario Lera, Alessandra Fanni, Augusto Montisci, An Auto‐
matic Optical Inspection System for the Diagnosis of Printed Circuits Based on Neu‐
ral Networks, Proceedings of the Industry Applications Conference, 2005, pp.
680-684
[176] Immanuel Edinbarough, Roberto Balderas, Subhash Bose, A Vision and Robot Based
On-Line Inspection Monitoring System for Electronic Manufacturing, Computers in
Industry, No. 56, 2005, pp. 986-996
[177] Du-Ming Tsai, Chien-Ta Lin, Fast Normalized Cross Correlation for Defect Detec‐
tion, Pattern Recognition Letters No. 24, 2003, pp. 2625-2631
[178] Chiu-Hui Chen, Chun-Chieh Wang, Chun-Yu Lin, Yu-Sen Shih, Chung-Fan Tu, Real‐
ization of Defect Automatic Inspection System for Flexible Printed Circuit (FPC), Pro‐
ceedings of the 35th International MATADOR Conference, 2007, pp. 225-228
[179] Sean P. Cunningham, Scott MacKinnon, Statistical Methods for Visual Defect Metrol‐
ogy, IEEE Transactions on Semiconductor Manufacturing, Vol. 11, No. 1, February
1998, pp. 48-53
439
440
Materials Science - Advanced Topics
[180] Dr. Kwangli Koh (Koh-Young), Eliminating False Calls With 3D AOI Technology,
EPP Europe, Issue 4, 2009, pp. 40-43
[181] Wei-Hung Su, Kebin Shi, Zhiwen Liu, Bo Wang, Karl Reichard, Shizhuo Yin, A
Large-Depth-of-Field Projected Fringe Profilometry Using Supercontinuum Light Il‐
lumination, Optics Express, Vol. 13, No. 3, February 2007, pp. 1025-1032
[182] Günther Wernicke, Matthias Dürr, Hartmut Gruber, Andreas Hermerschmidt, Sven
Krüger, Andreas Langner, High Resolution Optical Reconstruction of Digital Holo‐
grams, Fringe 2005, Session 4, 2005 pp. 480-487
[183] Liu Xiao-Li, Li A-Meng, Zhao Xiao-Bo, Gao Peng-Dong, Tian Jin-Dong, Peng Xiang,
Model-Based Optical Metrology and Visualization of 3-D Complex Objects, Optoe‐
lectronics Letters, Vol. 3, No.2, 15 Mar. 2007, pp. 115-118
[184] Marek Wegiel, Malgorzata Kujawinska, Fast 3D Shape Measurement System Based
on Color Structure Light Projection, Fringe 2005, Session 3, 2005, pp. 450-453
[185] Simon Davis, Lead-Free Inspection And Qualification With 3D AOI, OnBoard Tech‐
nology November 2005, pp. 54-57
[186] Zulki Khan, A Primer on AOI and AOT, Circuits Assembly, September 2006, pp.
38-41
[187] Dongwon Shin, Thomas R. Kurfess, Three-Dimensional Metrology of Surface Ex‐
tracted From a Cloud of Easured Points Using a New Point-to-Surface Assignment
Method: An Application to PCB-Mounted Solder Pastes, Precision Engineering Vol.
28, Issue 3, July 2004, pp. 302-313
[188] Tae-Hyeon Kim, Tai-Hoon Cho, Young Shik Moon, Sung Han Park, An Automated
Visual Inspection of Solder Joints Using 2D and 3D Features, Proceedings on the 3rd
IEEE Workshop on Applications of Computer Vision, 1996. pp. 110-115
[189] Deokhwa Hong, Hyunki Lee, Min Young Kim, Hyungsuck Cho, Jeon Il Moon, Sen‐
sor Fusion of Phase Measuring Profilometry and Stereo Vision for Three-Dimension‐
al Inspection of Electronic Components Assembled on Printed Circuit Boards,
Applied Optics, Vol. 48, No. 21, 20 July 2009, pp. 4158-4169
[190] S. S. Wong, K. L. Chan, 3D Object Model Reconstruction From Image Sequence Based
on Photometric Consistency in Volume Space, Pattern Analysis and Application, No‐
vember 2009, DOI 10.1007/s10044-009-0173
[191] Deokhwa Hong, Heechan Park, Hyungsuck Cho: Design of a Multi-Screen Deflec‐
tometer for Shape Measurement of Solder Joints on a PCB, Proceedings of the IEEE
International Symposium on Industrial Electronics (ISIE), 2009, pp 127-132
[192] Jiquan Ma, Solder Joint's Surface Recovery Based on Linear Hybrid Shape-FromShading, Proceedings of the Second Asia-Pacific Conference on Computational Intel‐
ligence and Industrial Applications (PACIIA), 2009, pp. 245-249
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
[193] Akira Kusano, Takashi Watanabe, Takuma Funahashi, Takayuki Fujiwara, Hiroyasu
Koshimizu, 3D Inspection of Electronic Devices by Means of Stereo Method on Single
Camera Environment, Proceedings of the Industrial Electronics (IECON), 2008, pp.
3391-3396
[194] Sheng Liu, Dathan Erdahl, I. Charles Ume, Achyuta Achari, Juergen Gamalski, A
Novel Approach for Flip Chip Solder Joint Quality Inspection: Laser Ultrasound and
Interferometric System, IEEE Transactions On Components and Packaging Technolo‐
gies, Vol. 24, No. 4, December 2001, pp 616-624
[195] Grantham K.H. Pang, Ming-Hei Chu, Automated Optical Inspection of Solder Paste
based on 2.5D Visual Images, Proceedings of the IEEE International Conference on
Mechatronics and Automation, 2009, pp 982-987
[196] Atsushi Teramoto, Takayuki Murakoshi, Masatoshi Tsuzaka, Hiroshi Fujit, Automat‐
ed Solder Inspection Technique for BGA-Mounted Substrates by Means of Oblique
Computed Tomography, IEEE Transactions on Electronics Packaging Manufactur‐
ing, Vol. 30, No. 4, October 2007, pp. 285-292
[197] Jun Cheng, Chi-Kit Ronald Chung, Edmund Y. Lam, Kenneth S. M. Fung, Fan Wang,
W. H. Leung, Structured-Light Based Sensing Using a Single Fixed Fringe Grating:
Fringe Boundary Detection and 3-D Reconstruction, IEEE Transactions on Electronics
Packaging Manufacturing, Vol. 31, No. 1, January 2008, pp. 19-31
[198] Yunxia Gao, Jun Wang, Testing Failure of Solder-Joints by ESPI on Board-Level Sur‐
face Mount Devices, Proceeding of the International Conference on Electronic Pack‐
aging Technology & High Density Packaging (ICEPT-HDP), 2009, pp. 1256-1259
441
Download PDF