Chapter 5: Fundamentals of Sampled Data Systems

Chapter 5: Fundamentals of Sampled Data Systems
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CHAPTER 5: FUNDAMENTALS OF
SAMPLED DATA SYSTEMS
INTRODUCTION
SECTION 5.1: CODING AND QUANTIZING
UNIPOLAR CODES
BIPOLAR CODES
COMPLEMENTARY CODES
DAC AND ADC STATIC TRANSFER FUNCTIONS AND DC
ERRORS
REFERENCES
SECTION 5.2: SAMPLING THEORY
THE NEED FOR A SAMPLE-AND-HOLD AMPLIFIER FUNCTION
THE NYQUIST CRITERIA
BASEBAND ANTIALIASING FILTERS
UNDERSAMPLING
ANTIALIASING FILTERS IN UNDERSAMPLING APPLICATIONS
REFERENCES
5.1
5.4
5.6
5.10
5.11
5.20
5.21
5.22
5.24
5.26
5.28
5.29
5.32
BASIC LINEAR DESIGN
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
CHAPTER 5: FUNDAMENTALS OF SAMPLED
DATA SYSTEMS
INTRODUCTION
To fully understand the specifications for converters it is beneficial to cover the
fundamentals of sampling theory.
SECTION 5.1: CODING AND QUANTIZING
Analog-to-digital converters (ADCs) translate analog measurements, which are
characteristic of most phenomena in the “real world,” to digital language, used in
information processing, computing, data transmission, and control systems. Digital-toanalog converters (DACs) are used in transforming transmitted or stored data, or the
results of digital processing, back to “real-world” variables for control, information
display, or further analog processing. The relationships between inputs and outputs of
ADCs and DACs are shown in Figure 5.1.
VREF
MSB
DIGITAL
INPUT
N-BITS
+FS
N-BIT
DAC
ANALOG
OUTPUT
LSB
RANGE
(SPAN)
0 OR –FS
VREF
MSB
+FS
RANGE
(SPAN)
0 OR –FS
ANALOG
INPUT
N-BIT
ADC
DIGITAL
OUTPUT
N-BITS
LSB
Figure 5.1: Analog-to-Digital Converter (ADC) and Digital-to-Analog Converter
(DAC) Input and Output Definitions
Analog input variables, whatever their origin, are most frequently converted by
transducers into voltages or currents. These electrical quantities may appear as fast or
slow “dc” continuous direct measurements of a phenomenon in the time domain, as
5.1
BASIC LINEAR DESIGN
modulated ac waveforms (using a wide variety of modulation techniques), or in some
combination, with a spatial configuration of related variables to represent shaft angles.
Examples of the first are outputs of thermocouples, potentiometers on dc references, and
analog computing circuitry; of the second, “chopped” optical measurements, ac strain
gage or bridge outputs, and digital signals buried in noise; and of the third, synchros and
resolvers.
The analog variables to be dealt with in this chapter are those involving voltages or
currents representing the actual analog phenomena. They may be either wideband or
narrowband. They may be either scaled from the direct measurement, or subjected to
some form of analog preprocessing, such as linearization, combination, demodulation,
filtering, sample-hold, etc.
As part of the process, the voltages and currents are “normalized” to ranges compatible
with assigned ADC input ranges. Analog output voltages or currents from DACs are
direct and in normalized form, but they may be subsequently post-processed (e.g., scaled,
filtered, amplified, etc.).
Information in digital form is normally represented by arbitrarily fixed voltage levels
referred to “ground,” either occurring at the outputs of logic gates, or applied to their
inputs. The digital numbers used are all basically binary; that is, each “bit,” or unit of
information has one of two possible states. These states are “off,” “false,” or “0,” and
“on,” “true,” or “1.” It is also possible to represent the two logic states by two different
levels of current, however this is much less popular than using voltages. There is also no
particular reason why the voltages need be referenced to ground—as in the case of
emitter coupled logic (ECL), positive emitter coupled logic (PECL) or low voltage
differential signaling logic (LVDS) for example.
Words are groups of levels representing digital numbers; the levels may appear
simultaneously in parallel, on a bus or groups of gate inputs or outputs, serially (or in a
time sequence) on a single line, or as a sequence of parallel bytes (i.e., “byte-serial”) or
nibbles (small bytes). For example, a 16-bit word may occupy the 16 bits of a 16-bit bus,
or it may be divided into two sequential bytes for an 8-bit bus, or four 4-bit nibbles for a
4-bit bus.
A unique parallel or serial grouping of digital levels, or a number, or code, is assigned to
each analog level which is quantized (i.e., represents a unique portion of the analog
range). A typical digital code would be this array:
a7 a6 a5 a4 a3 a2 a1 a0 = 1 0 1 1 1 0 0 1
It is composed of eight bits. The “1” at the extreme left is called the “most significant bit”
(MSB, or Bit 1), and the one at the right is called the "least significant bit" (LSB, or bit
N: 8 in this case). The meaning of the code, as a number, a character, or a representation
of an analog variable, is unknown until the code and the conversion relationship have
been defined. It is important not to confuse the designation of a particular bit (i.e., Bit 1,
Bit 2, etc.) with the subscripts associated with the “a” array. The subscripts correspond to
power of 2 associated with the weight of a particular bit in the sequence.
5.2
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
The best-known code is natural or straight binary (base 2). Binary codes are most
familiar in representing integers; i.e., in a natural binary integer code having N bits, the
LSB has a weight of 20 (i.e., 1), the next bit has a weight of 21 (i.e., 2), and so on up to
the MSB, which has a weight of 2N–1 (i.e., 2N/2). The value of a binary number is
obtained by adding up the weights of all non-zero bits. When the weighted bits are added
up, they form a unique number having any value from 0 to 2N–1.
Often, for convenience, a binary number is expressing in hexadecimal (base 16). This
reduces the length of the word and makes it easier to read. Fig. 5.2 shows the
relationship between binary and hexadecimal (commonly referred to as “hex”).
BINARY
0000
0001
0010
0011
0100
0101
0110
0111
HEX
0
1
2
3
4
5
6
7
BINARY
1000
1001
1010
1011
1100
1101
1110
1111
HEX
8
9
A
B
C
D
E
F
Figure 5.2: The Relationship Between Binary and Hexadecimal
WHOLE NUMBERS:
Number10 = aN–12N–1 + aN –22N–2 + … +a121 + a020
MSB
LSB
Example: 1011 2 = (1×2 3) + (0×22)+ (1×2 1)+ (1×2 0)
= 8
+ 0
+ 2 + 1
= 1110
FRACTIONAL NUMBERS:
Number10 = aN–12–1 + aN–2 2 –2 + … + a12–(N–1) + a02–N
MSB
LSB
Example: 0.1011 2 = (1×0.5) + (0×0.25) + (1×0.125) + (1×0.0625)
= 0.5 +
0
+ 0.125
+ 0.0625 = 0.687510
Figure 5.3: Representing a Base-10 Number with a Binary Number (Base-2)
In converter technology, full-scale (abbreviated FS) is independent of the number of bits
of resolution, N. A more useful coding is fractional binary which is always normalized to
full-scale. Integer binary can be interpreted as fractional binary if all integer values are
divided by 2N. For example, the MSB has a weight of ½ (i.e., 2(N–1)/2N = 2–1), the next bit
has a weight of ¼ (i.e., 2–2, and so forth down to the LSB, which has a weight of 1/2N
(i.e., 2–N). When the weighted bits are added up, they form a number with any of 2N
5.3
BASIC LINEAR DESIGN
values, from 0 to (1 – 2–N) of full-scale. Additional bits simply provide more fine
structure without affecting full-scale range. The relationship between base-10 numbers
and binary numbers (base-2) are shown in Figure 5.3 along with examples of each.
Unipolar Codes
In data conversion systems, the coding method must be related to the analog input range
(or span) of an ADC or the analog output range (or span) of a DAC. The simplest case is
when the input to the ADC or the output of the DAC is always a unipolar positive voltage
(current outputs are very popular for DAC outputs, much less for ADC inputs). The most
popular code for this type of signal is straight binary and is shown in Figure 5.3 for a 4bit converter. Notice that there are 16 distinct possible levels, ranging from the all-zeros
code 0000, to the all-ones code 1111. It is important to note that the analog value
represented by the all-ones code is not full-scale (abbreviated FS), but FS – 1 LSB. This
is a common convention in data conversion notation and applies to both ADCs and
DACs. Figure 5.4 gives the base-10 equivalent number, the value of the base-2 binary
code relative to full-scale (FS), and also the corresponding voltage level for each code
(assuming a +10 V full-scale converter).
BASE 10
NUMBER
+15
+14
+13
+12
+11
+10
+9
+8
+7
+6
+5
+4
+3
+2
+1
0
SCALE
+10 V FS
BINARY
+FS – 1 LSB = 15/16 FS
+7/8 FS
+13/16 FS
+3/4 FS
+11/16 FS
+5/16 FS
+9/16 FS
+1/2 FS
+7/16 FS
+3/8 FS
+5/16 FS
+1/4 FS
+3/16 FS
+1/8 FS
1 LSB = +1/16 FS
0
9.375
8.750
8.125
7.500
6.875
6.250
5.625
5.000
4.375
3.750
3.125
2.500
1.875
1.250
0.625
0.000
1111
1110
1101
1100
1011
1010
1001
1000
0111
0110
0101
0100
0011
0010
0001
0000
Figure 5.4: Unipolar Binary Code, 4-Bit Converter
5.4
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
Figure 5.5 shows the transfer function for an ideal 3-bit DAC with straight binary input
coding. Notice that the analog output is zero for the all-zeros input code. As the digital
input code increases, the analog output increases 1 LSB (1/8 scale in this example) per
code. The most positive output voltage is 7/8 FS, corresponding to a value equal to FS –
1 LSB. The mid-scale output of 1/2 FS is generated when the digital input code is 100.
FS
7/8
3/4
ANALOG
OUTPUT
5/8
1/2
3/8
1/4
1/8
0
000
001
010
011
100
101
110
111
DIGITAL INPUT (STRAIGHT BINARY)
Figure 5.5: Transfer Function for Ideal Unipolar 3-Bit DAC
111
110
DIGITAL
OUTPUT
(STRAIGHT
BINARY)
101
100
1 LSB
011
1/2 LSB
010
001
000
0
1/8
1/4
3/8
1/2
5/8
3/4
7/8
FS
ANALOG INPUT
Figure 5.6: Transfer Function for Ideal 3-Bit Unipolar ADC
5.5
BASIC LINEAR DESIGN
The transfer function of an ideal 3-bit ADC is shown in Figure 5.6. There is a range of
analog input voltage over which the ADC will produce a given output code, and this
range is the quantization uncertainty and is equal to 1 LSB. Note that the width of the
transition regions between adjacent codes is zero for an ideal ADC. In practice, however,
there is always transition noise associated with these levels, and therefore the width is
non-zero. It is customary to define the analog input corresponding to a given code by the
code center which lies halfway between two adjacent transition regions (illustrated by the
black dots in the diagram). This requires that the first transition region occur at ½ LSB.
The full-scale analog input voltage is defined by 7/8 FS, (FS – 1 LSB).
Bipolar Codes
In many systems, it is desirable to represent both positive and negative analog quantities
with binary codes. Either offset binary, twos complement, ones complement, and sign
magnitude codes will accomplish this, but offset binary and twos complement are by far
the most popular. The relationships between these codes for a 4-bit system is shown in
Figure 5.7. Note that the values are scaled for a ±5 V full-scale input/output voltage
range.
BASE 10
NUMBER
SCALE
±5V FS
OFFSET
BINARY
TWOS
COMP.
+7
+6
+5
+4
+3
+2
+1
0
–1
–2
–3
–4
–5
–6
–7
–8
+FS – 1LSB = +7/8 FS
+3/4 FS
+5/8 FS
+1/2 FS
+3/8 FS
+1/4 FS
+1/8 FS
0
– 1/8 FS
– 1/4 FS
– 3/8 FS
–1/2 FS
–5/8 FS
–3/4 FS
– FS + 1LSB = –7/8 FS
– FS
+4.375
+3.750
+3.125
+2.500
+1.875
+1.250
+0.625
0.000
–0.625
–1.250
–1.875
–2.500
–3.125
–3.750
–4.375
–5.000
1111
1110
1101
1100
1011
1010
1001
1000
0111
0110
0101
0100
0011
0010
0001
0000
0111
0110
0101
0100
0011
0010
0001
0000
1111
1110
1101
1100
1011
1010
1001
1000
CODES NOT NORMALLY USED
IN COMPUTATIONS (SEE TEXT)
*
0+
0–
ONES
COMP.
0111
0110
0101
0100
0011
0010
0001
*0 0 0 0
1110
1101
1100
1011
1010
1001
1000
ONES
COMP.
0000
1111
Figure 5.7: Bipolar Codes, 4-Bit Converter
5.6
SIGN
MAG.
0111
0110
0101
0100
0011
0010
0001
*1 0 0 0
1001
1010
1011
1100
1101
1110
1111
SIGN
MAG.
0000
1000
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
For offset binary, the zero signal value is assigned the code 1000. The sequence of codes
is identical to that of straight binary. The only difference between a straight and offset
binary system is the half-scale offset associated with analog signal. The most negative
value (–FS + 1 LSB) is assigned the code 0001, and the most positive value (+FS – 1
LSB) is assigned the code 1111. Note that in order to maintain perfect symmetry about
mid-scale, the all-zeros code (0000) representing negative full-scale (–FS) is not normally
used in computation. It can be used to represent a negative off-range condition or simply
assigned the value of the 0001 (–FS + 1 LSB).
The relationship between the offset binary code and the analog output range of a bipolar
3-bit DAC is shown in Figure 5.8. The analog output of the DAC is zero for the zerovalue input code 100. The most negative output voltage is generally defined by the 001
code (–FS + 1 LSB), and the most positive by 111 (+FS – 1 LSB). The output voltage for
the 000 input code is available for use if desired, but makes the output nonsymmetrical
about zero and complicates the mathematics.
The offset binary output code a bipolar 3-bit ADC as a function of its analog input is
shown in Figure 5.9. Note that zero analog input defines the center of the mid-scale code
100. As in the case of bipolar DACs, the most negative input voltage is generally defined
by the 001 code (–FS + 1 LSB), and the most positive by 111 (+FS – 1 LSB). As
discussed above, the 000 output code is available for use if desired, but makes the output
nonsymmetrical about zero and complicates the mathematics.
+FS
+3/8
+1/4
ANALOG
OUTPUT
+1/8
0
–1/8
–1/4
–3/8
–FS
000
001
010
011
100
101
110
111
DIGITAL INPUT (OFFSET BINARY)
Figure 5.8: Transfer Function for Ideal Bipolar 3-Bit DAC
5.7
BASIC LINEAR DESIGN
Twos complement is identical to offset binary with the most significant bit (MSB)
complemented (inverted). This is obviously very easy to accomplish in a data converter,
using a simple inverter or taking the complementary output of a “D” flip-flop. The
popularity of twos complement coding lies in the ease with which mathematical
operations can be performed in computers. Twos complement, for conversion purposes,
consists of a binary code for positive magnitudes (0 sign bit), and the twos complement
of each positive number to represent its negative. The twos complement is formed
arithmetically by complementing the number and adding 1 LSB. For example, –3/8 FS is
obtained by taking the twos complement of +3/8 FS. This is done by first complementing
+3/8 FS, 0011 obtaining 1100. Adding 1 LSB, we obtain 1101.
Twos complement makes subtraction easy. For example, to subtract 3/8 FS from 4/8 FS,
add 4/8 to –3/8, or 0100 to 1101. The result is 0001, disregarding the extra carry, or 1/8.
Ones complement can also be used to represent negative numbers, although it is much
less popular than twos complement and rarely used today. The ones complement is
obtained by simply complementing all of a positive number’s digits. For instance, the
ones complement of 3/8 FS (0011) is 1100. A ones complemented code can be formed by
complementing each positive value to obtain its corresponding negative value. This
includes zero, which is then represented by either of two codes, 0000 (referred to as 0+)
and 1111 (referred to as 0–). This ambiguity must be dealt with mathematically, and
presents obvious problems relating to ADCs and DACs for which there is a single code
which represents zero.
111
110
DIGITAL
OUTPUT
(OFFSET
BINARY)
101
100
011
010
001
000
–FS
–3/8
–1/4
–1/8
0
+1/8 +1/4
+3/8
+FS
ANALOG INPUT
Figure 5.9: Transfer Function for Ideal 3-Bit Bipolar ADC
5.8
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
Sign-magnitude would appear to be the most straightforward way of expressing signed
analog quantities digitally. Simply determine the code appropriate for the magnitude and
add a polarity bit. Sign-magnitude BCD is popular in bipolar digital voltmeters, but has
the problem of two allowable codes for zero. It is therefore unpopular for most
applications involving ADCs or DACs.
Figure 5.10 summarizes the relationships between the various bipolar codes: offset
binary, twos complement, ones complement, and sign-magnitude and shows how to
covert between them.
The last code to be considered in this section is binary-coded-decimal (BCD), where each
base-10 digit (0 to 9) in a decimal number is represented as the corresponding 4-bit
straight binary word as shown in Figure 5.11. The minimum digit 0 is represented as
0000, and the digit 9 by 1001. This code is relatively inefficient, since only 10 of the 16
code states for each decade are used. It is, however, a very useful code for interfacing to
decimal displays such as in digital voltmeters.
To Convert From
To
Sign Magnitude
2's Complement
Sign Magnitude
No
Change
If MSB = 1,
complement
other bits,
add 00…01
2's Complement
If MSB = 1,
complement
other bits,
add 00…01
No
Change
Offset binary
Complement MSB
If new MSB = 0
complement
other bits,
add 00…01
Complement
MSB
1's Complement
If MSB = 1,
complement
other bits
If MSB = 1,
add 11…11
Offset Binary
Complement MSB
If new MSB = 1,
complement
other bits,
add 00…01
Complement
MSB
No
Change
Complement MSB
If new MSB = 1,
add 11…11
1's Complement
If MSB = 1,
complement
other bits
If MSB = 1,
add 00…01
Complement MSB
If new MSB = 0,
add 00…01
No
Change
Figure 5.10: Relationships among Bipolar Codes
5.9
BASIC LINEAR DESIGN
BASE 10
NUMBER
SCALE
+15 +FS – 1LSB = +15/16 FS
+7/8 FS
+14
+13/16 FS
+13
+3/4 FS
+12
+11/16 FS
+11
+5/8 FS
+10
+9/16 FS
+9
+1/2 FS
+8
+7/16 FS
+7
+3/8 FS
+6
+5/16 FS
+5
+1/4 FS
+4
+3/16 FS
+3
+1/8 FS
+2
1LSB = +1/16 FS
+1
0
0
+10V FS
9.375
8.750
8.125
7.500
6.875
6.250
5.625
5.000
4.375
3.750
3.125
2.500
1.875
1.250
0.625
0.000
DECADE 1 DECADE 2 DECADE 3 DECADE 4
1001
1000
1000
0111
0110
0110
0101
0101
0100
0011
0011
0010
0001
0001
0000
0000
0011
0111
0001
0101
1000
0010
0110
0000
0011
0111
0001
0101
1000
0010
0110
0000
0111
0101
0010
0000
0111
0101
0010
0000
0111
0101
0010
0000
0111
0101
0010
0000
0101
0000
0101
0000
0101
0000
0101
0000
0101
0000
0101
0000
0101
0000
0101
0000
Figure 5.11: Binary Coded Decimal (BCD) Code
Complementary Codes
Some forms of data converters (for example, early DACs using monolithic NPN quad
current switches), require standard codes such as natural binary or BCD, but with all bits
represented by their complements. Such codes are called complementary codes. All the
codes discussed thus far have complementary codes which can be obtained by this
method.
In a 4-bit complementary-binary converter, 0 is represented by 1111, half-scale by 0111,
and FS – 1 LSB by 0000. In practice, the complementary code can usually be obtained by
using the complementary output of a register rather than the true output, since both are
available.
Sometimes the complementary code is useful in inverting the analog output of a DAC.
Today many DACs provide differential outputs which allow the polarity inversion to be
accomplished without modifying the input code. Similarly, many ADCs provide
differential logic inputs which can be used to accomplish the polarity inversion.
5.10
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
DAC and ADC Static Transfer Functions and DC Errors
The most important thing to remember about both DACs and ADCs is that either the
input or output is digital, and therefore the signal is quantized. That is, an N-bit word
represents one of 2N possible states, and therefore an N-bit DAC (with a fixed reference)
can have only 2N possible analog outputs, and an N-bit ADC can have only 2N possible
digital outputs. As previously discussed, the analog signals will generally be voltages or
currents.
The resolution of data converters may be expressed in several different ways: the weight
of the least significant bit (LSB), parts per million of full-scale (ppm FS), millivolts
(mV), etc. Different devices (even from the same manufacturer) will be specified
differently, so converter users must learn to translate between the different types of
specifications if they are to compare devices successfully. The size of the least significant
bit for various resolutions is shown in Figure 5.12.
RESOLUTION
N
2N
VOLTAGE
(10V FS)
ppm FS
% FS
dB FS
2-bit
4
2.5 V
250,000
25
– 12
4-bit
16
625 mV
62,500
6.25
– 24
6-bit
64
156 mV
15,625
1.56
– 36
8-bit
256
39.1 mV
3,906
0.39
– 48
10-bit
1,024
9.77 mV (10 mV)
977
0.098
– 60
12-bit
244
0.024
– 72
– 84
4,096
2.44 mV
14-bit
16,384
610 μV
61
0.0061
16-bit
65,536
153 μV
15
0.0015
– 96
18-bit
262,144
38 μV
4
0.0004
– 108
20-bit
1,048,576
9.54 μV (10 μV)
1
0.0001
– 120
22-bit
4,194,304
2.38 μV
0.24
0.000024
– 132
24-bit
16,777,216
596 nV*
0.06
0.000006
– 144
*600nV is the Johnson Noise in a 10kHz BW of a 2.2kΩ Resistor @ 25°C
Remember: 10-bits and 10V FS yields an LSB of 10mV, 1000ppm, or 0.1%.
All other values may be calculated by powers of 2.
Figure 5.12: Quantization: The Size of a Least Significant Bit (LSB)
Before we can consider the various architectures used in data converters, it is necessary
to consider the performance to be expected, and the specifications which are important.
The following sections will consider the definition of errors and specifications used for
data converters. This is important in understanding the strengths and weaknesses of
different ADC/DAC architectures.
Figure 5.13 shows the ideal transfer characteristics for a 3-bit unipolar DAC and a 3-bit
unipolar ADC. In a DAC, both the input and the output are quantized, and the graph
5.11
BASIC LINEAR DESIGN
consists of eight points—while it is reasonable to discuss the line through these points, it
is very important to remember that the actual transfer characteristic is not a line, but a
number of discrete points.
DAC
FS
ADC
111
110
DIGITAL
OUTPUT
ANALOG
OUTPUT
101
100
011
QUANTIZATION
UNCERTAINTY
010
QUANTIZATION
UNCERTAINTY
001
000
000
001
010
011
100
101
DIGITAL INPUT
110
111
ANALOG INPUT
FS
Figure 5.13: Transfer Functions for Ideal 3-Bit DAC and ADC
The input to an ADC is analog and is not quantized, but its output is quantized. The
transfer characteristic therefore consists of eight horizontal steps. When considering the
offset, gain, and linearity of an ADC we consider the line joining the midpoints of these
steps—often referred to as the code centers.
For both DACs and ADCs, digital full-scale (all 1s) corresponds to 1 LSB below the
analog full-scale (FS). The (ideal) ADC transitions take place at ½ LSB above zero, and
thereafter every LSB, until 1½ LSB below analog full-scale. Since the analog input to an
ADC can take any value, but the digital output is quantized, there may be a difference of
up to ½ LSB between the actual analog input and the exact value of the digital output.
This is known as the quantization error or quantization uncertainty as shown in Figure
5.15. In ac (sampling) applications this quantization error gives rise to quantization noise
which will be discussed in Section 5.3 of this chapter.
As previously discussed, there are many possible digital coding schemes for data
converters: straight binary, offset binary, 1s complement, 2s complement, sign
magnitude, gray code, BCD, and others. This section, being devoted mainly to the analog
issues surrounding data converters, will use simple binary and offset binary in its
examples and will not consider the merits and disadvantages of these, or any other forms
of digital code.
The examples in Figure 5.13 use unipolar converters, whose analog port has only a single
polarity. These are the simplest type, but bipolar converters are generally more useful in
real-world applications. There are two types of bipolar converters: the simpler is merely a
unipolar converter with an accurate 1 MSB of negative offset (and many converters are
5.12
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
arranged so that this offset may be switched in and out so that they can be used as either
unipolar or bipolar converters at will), but the other, known as a sign-magnitude
converter is more complex, and has N bits of magnitude information and an additional bit
which corresponds to the sign of the analog signal. Sign-magnitude DACs are quite rare,
and sign-magnitude ADCs are found mostly in digital voltmeters (DVMs). The unipolar,
offset binary, and sign-magnitude representations are shown in Figure 5.14.
UNIPOLAR
OFFSET BIPOLAR
FS – 1 LSB
FS – 1 LSB
0
SIGN MAGNITUDE
BIPOLAR
CODE
FS – 1 LSB
CODE
0
ALL
"1"s
CODE
0
ALL
"1"s
1 AND ALL "0"s
–FS
–(FS – 1 LSB)
Figure 5.14: Unipolar and Bipolar Converters
The four dc errors in a data converter are offset error, gain error, and two types of
linearity error (differential and integral). Offset and gain errors are analogous to offset
and gain errors in amplifiers as shown in Figure 5.15 for a bipolar input range. (Though
offset error and zero error, which are identical in amplifiers and unipolar data converters,
are not identical in bipolar converters and should be carefully distinguished.). The
transfer characteristics of both DACs and ADCs may be expressed as D = K + GA, where
D is the digital code, A is the analog signal, and K and G are constants. In a unipolar
converter, K is zero, and in an offset bipolar converter, it is –1 MSB. The offset error is
the amount by which the actual value of K differs from its ideal value.
The gain error is the amount by which G differs from its ideal value, and is generally
expressed as the percentage difference between the two, although it may be defined as the
gain error contribution (in mV or LSB) to the total error at full-scale. These errors can
usually be trimmed by the data converter user. Note, however, that amplifier offset is
trimmed at zero input, and then the gain is trimmed near to full-scale. The trim algorithm
for a bipolar data converter is not so straightforward.
5.13
BASIC LINEAR DESIGN
+FS
+FS
ACTUAL
ACTUAL
IDEAL
IDEAL
0
0
ZERO ERROR
OFFSET
ERROR
ZERO ERROR
NO GAIN ERROR:
ZERO ERROR = OFFSET ERROR
–FS
–FS
WITH GAIN ERROR:
OFFSET ERROR = 0
ZERO ERROR RESULTS
FROM GAIN ERROR
Figure 5.15: Data Converter Offset and Gain Error
The integral linearity error of a converter is also analogous to the linearity error of an
amplifier, and is defined as the maximum deviation of the actual transfer characteristic of
the converter from a straight line, and is generally expressed as a percentage of full-scale
(but may be given in LSBs). For an ADC, the most popular convention is to draw the
straight line through the midpoints of the codes, or the code centers. There are two
common ways of choosing the straight line: endpoint and best straight line as shown in
Figure 5.16.
END POINT METHOD
BEST STRAIGHT LINE METHOD
OUTPUT
LINEARITY
ERROR = X
INPUT
LINEARITY
ERROR ≈ X/2
INPUT
Figure 5.16: Method of Measuring Integral Linearity Errors
(Same Converter on Both Graphs)
5.14
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
In the endpoint system, the deviation is measured from the straight line through the origin
and the full-scale point (after gain adjustment). This is the most useful integral linearity
measurement for measurement and control applications of data converters (since error
budgets depend on deviation from the ideal transfer characteristic, not from some
arbitrary “best fit”), and is the one normally adopted by Analog Devices, Inc.
The best straight line, however, does give a better prediction of distortion in ac
applications, and also gives a lower value of “linearity error” on a data sheet. The best fit
straight line is drawn through the transfer characteristic of the device using standard
curve fitting techniques, and the maximum deviation is measured from this line. In
general, the integral linearity error measured in this way is only 50% of the value
measured by endpoint methods. This makes the method good for producing impressive
data sheets, but it is less useful for error budget analysis. For ac applications, it is even
better to specify distortion than dc linearity, so it is rarely necessary to use the best
straight line method to define converter linearity.
The other type of converter nonlinearity is differential nonlinearity (DNL). This relates to
the linearity of the code transitions of the converter. In the ideal case, a change of 1 LSB
in digital code corresponds to a change of exactly 1 LSB of analog signal. In a DAC, a
change of 1 LSB in digital code produces exactly 1 LSB change of analog output, while
in an ADC there should be exactly 1 LSB change of analog input to move from one
digital transition to the next. Differential linearity error is defined as the maximum
amount of deviation of any quantum (or LSB change) in the entire transfer function from
its ideal size of 1 LSB.
Where the change in analog signal corresponding to 1 LSB digital change is more or less
than 1 LSB, there is said to be a DNL error. The DNL error of a converter is normally
defined as the maximum value of DNL to be found at any transition across the range of
the converter. Figure 5.17 shows the nonideal transfer functions for a DAC and an ADC
and shows the effects of the DNL error.
DAC
FS
ADC
111
110
ANALOG
OUTPUT
DIGITAL
OUTPUT
NON-MONOTONIC
101
100
MISSING CODE
011
010
001
000
000
001
010
011
100
101
110
111
ANALOG INPUT
FS
DIGITAL INPUT
Figure 5.17: Transfer Functions for Non-Ideal 3-Bit DAC and ADC
5.15
BASIC LINEAR DESIGN
The DNL of a DAC is examined more closely in Figure 5.18. If the DNL of a DAC is
less than –1 LSB at any transition, the DAC is nonmonotonic i.e., its transfer
characteristic contains one or more localized maxima or minima. A DNL greater than
+1 LSB does not cause nonmonotonicity, but is still undesirable. In many DAC
applications (especially closed-loop systems where nonmonotonicity can change negative
feedback to positive feedback), it is critically important that DACs are monotonic. DAC
monotonicity is often explicitly specified on data sheets, although if the DNL is
guaranteed to be less than 1 LSB (i.e., |DNL| ≤ 1 LSB) then the device must be
monotonic, even without an explicit guarantee.
FS
d
1 LSB,
DNL = 0
BIT 2 IS 1 LSB HIGH
BIT 1 IS 1 LSB LOW
2 LSB,
DNL = +1 LSB
ANALOG
OUTPUT
1 LSB,
DNL = 0
2 LSB,
DNL = +1 LSB
1 LSB,
DNL = 0
d
000
001
–1 LSB,
DNL = –2 LSB
1 LSB,
DNL = 0
NON-MONOTONIC IF
DNL < –1 LSB
010 011
100 101 110
DIGITAL CODE INPUT
111
Figure 5.18: Details of DAC Differential Nonlinearity
In Figure 5.19, the DNL of an ADC is examined more closely on an expanded scale.
ADCs can be nonmonotonic, but a more common result of excess DNL in ADCs is
missing codes. Missing codes in an ADC are as objectionable as nonmonotonicity in a
DAC. Again, they result from DNL < –1 LSB.
Not only can ADCs have missing codes, they can also be nonmonotonic as shown in
Figure 5.22. As in the case of DACs, this can present major problems—especially in
servo applications.
In a DAC, there can be no missing codes—each digital input word will produce a
corresponding analog output. However, DACs can be nonmonotonic as previously
discussed. In a straight binary DAC, the most likely place a nonmonotonic condition can
develop is at midscale between the two codes: 011…11 and 100…00. If a nonmonotonic
5.16
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
condition occurs here, it is generally because the DAC is not properly calibrated or
trimmed. A successive approximation ADC with an internal nonmonotonic DAC will
generally produce missing codes but remain monotonic. However it is possible for an
ADC to be nonmonotonic—again depending on the particular conversion architecture.
Figure 5.20 shows the transfer function of an ADC which is nonmonotonic and has a
missing code.
MISSING CODE (DNL < –1 LSB)
DIGITAL
OUTPUT
CODE
1 LSB,
DNL = 0
0.25 LSB,
DNL = –0.75 LSB
1.5 LSB,
DNL = +0.5 LSB
0.5 LSB,
DNL = –0.5 LSB
1 LSB,
DNL = 0
ANALOG INPUT
Figure 5.19: Details of ADC Differential Nonlinearity
MISSING CODE
DIGITAL
OUTPUT
CODE
NON-MONOTONIC
ANALOG INPUT
Figure 5.20: Nonmonotonic ADC with Missing Code
5.17
BASIC LINEAR DESIGN
ADCs which use the subranging architecture divide the input range into a number of
coarse segments, and each coarse segment is further divided into smaller segments—and
ultimately the final code is derived. This process is described in more detail in Chapter 3
of this book. An improperly trimmed subranging ADC may exhibit nonmonotonicity,
wide codes, or missing codes at the subranging points as shown in Figure 5.21 A, B, and
C, respectively. This type of ADC should be trimmed so that drift due to aging or
temperature produces wide codes at the sensitive points rather than nonmonotonic or
missing codes.
(A) NON-MONOTONIC
(B) WIDE CODES
(C) MISSING CODES
NON-MONOTONIC
MISSING
CODE
WIDE
CODE
NON-MONOTONIC
MISSING
CODE
WIDE
CODE
ANALOG INPUT
Figure 5.21: Errors Associated with Improperly Trimmed Subranging ADC
Defining missing codes is more difficult than defining nonmonotonicity. All ADCs suffer
from some inherent transition noise as shown in Figure 5.22 (think of it as the flicker
between adjacent values of the last digit of a DVM). As resolutions and bandwidths
become higher, the range of input over which transition noise occurs may approach, or
even exceed, 1 LSB. High resolution wideband ADCs generally have internal noise
sources which can be reflected to the input as effective input noise summed with the
signal. The effect of this noise, especially if combined with a negative DNL error, may be
that there are some (or even all) codes where transition noise is present for the whole
range of inputs. There are therefore some codes for which there is no input which will
guarantee that code as an output, although there may be a range of inputs which will
sometimes produce that code.
5.18
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
CODING AND QUANTIZING
CODE TRANSITION NOISE
DNL
TRANSITION NOISE
AND DNL
ADC
OUTPUT
CODE
ADC INPUT
ADC INPUT
ADC INPUT
Figure 5.22: Combined Effects of Code Transition Noise and DNL
For low resolution ADCs, it may be reasonable to define no missing codes as a
combination of transition noise and DNL which guarantees some level (perhaps 0.2 LSB)
of noise-free code for all codes. However, this is impossible to achieve at the very high
resolutions achieved by modern sigma-delta ADCs, or even at lower resolutions in wide
bandwidth sampling ADCs. In these cases, the manufacturer must define noise levels and
resolution in some other way. Which method is used is less important, but the data sheet
should contain a clear definition of the method used and the performance to be expected.
The discussion thus far has not dealt with the most important dc specifications associated
with data converters. Other less important specifications require only a definition. There
are also AC specifications. Converter specifications are covered in Chapter 3.
5.19
BASIC LINEAR DESIGN
REFERENCES:
CODING AND QUANTIZATION
1.
K. W. Cattermole, Principles of Pulse Code Modulation, American Elsevier Publishing Company,
Inc., 1969, New York NY, ISBN 444-19747-8. (An excellent tutorial and historical discussion of data
conversion theory and practice, oriented towards PCM, but covers practically all aspects. This one is
a must for anyone serious about data conversion! Try internet secondhand bookshops such as
http://www.abebooks.com for starters).
2.
Frank Gray, “Pulse Code Communication,” U.S. Patent 2,632,058, filed November 13, 1947, issued
March 17, 1953. (Detailed patent on the Gray code and its application to electron beam coders).
3.
R. W. Sears, “Electron Beam Deflection Tube for Pulse Code Modulation,” Bell System Technical
Journal, Vol. 27, pp. 44-57, Jan. 1948. (Describes an electron-beam deflection tube 7-bit, 100 kSPS
flash converter for early experimental PCM work).
4.
J. O. Edson and H. H. Henning, “Broadband Codecs for an Experimental 224 Mb/s PCM Terminal,”
Bell System Technical Journal, Vol. 44, pp. 1887-1940, Nov. 1965. (Summarizes experiments on
ADCs based on the electron tube coder as well as a bit-per-stage Gray code 9-bit solid state ADC. The
electron beam coder was 9 bits at 12 MSPS, and represented the fastest of its type).
5.
Dan Sheingold, Analog-Digital Conversion Handbook, 3rd Edition, Analog Devices and PrenticeHall, 1986, ISBN-0-13-032848-0. (The defining and classic book on data conversion).
5.20
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
SAMPLING THEORY
SECTION 5.2: SAMPLING THEORY
This section discusses the basics of sampling theory. A block diagram of a typical realtime sampled data system is shown in Figure 5.23. Prior to the actual analog-to-digital
conversion, the analog signal usually passes through some sort of signal conditioning
circuitry which performs such functions as amplification, attenuation, and filtering. The
low-pass/band-pass filter is required to remove unwanted signals outside the bandwidth
of interest and prevent aliasing.
fs
fs
fa
LPF
OR
BPF
N-BIT
ADC
AMPLITUDE
QUANTIZATION
DSP
LPF
OR
BPF
N-BIT
DAC
DISCRETE
TIME SAMPLING
fa
ts=
1
fs
t
Figure 5.23: Sampled Data System
The system shown in Figure 5.23 is a real-time system, i.e., the signal to the ADC is
continuously sampled at a rate equal to fs, and the ADC presents a new sample to the
DSP at this rate. In order to maintain real-time operation, the DSP must perform all its
required computation within the sampling interval, 1/fs, and present an output sample to
the DAC before arrival of the next sample from the ADC. An example of a typical DSP
function would be a digital filter.
In the case of FFT analysis, a block of data is first transferred to the DSP memory. The
FFT is calculated at the same time a new block of data is transferred into the memory, in
order to maintain real-time operation. The DSP must calculate the FFT during the data
transfer interval so it will be ready to process the next block of data.
Note that the DAC is required only if the DSP data must be converted back into an
analog signal (as would be the case in a voice-band or audio application, for example).
There are many applications where the signal remains entirely in digital format after the
5.21
BASIC LINEAR DESIGN
initial A/D conversion. Similarly, there are applications where the DSP is solely
responsible for generating the signal to the DAC, such as in CD player electronics. If a
DAC is used, it must be followed by an analog anti-imaging filter to remove the image
frequencies. Finally, there are slower speed industrial process control systems where
sampling rates are much lower—regardless of the system, the fundamentals of sampling
theory still apply.
There are two key concepts involved in the actual analog-to-digital and digital-to-analog
conversion process: discrete time sampling and finite amplitude resolution due to
quantization. An understanding of these concepts is vital to data converter applications.
The Need for a Sample-and-Hold Amplifier Function
The generalized block diagram of a sampled data system shown in Figure 5.23 assumes
some type of ac signal at the input. It should be noted that this does not necessarily have
to be so, as in the case of modern digital voltmeters (DVMs) or ADCs optimized for dc
measurements, but for this discussion assume that the input signal has some upper
frequency limit fa.
ANALOG INPUT
2N
v(t) = q
2
sin (2π f t )
2N
dv
q
2π f cos (2π f t )
=
2
dt
dv
q 2(N–1) 2π f
dt max =
fmax =
fmax =
dv
dt max
2(N–1) 2π q
dv
dt max
N-BIT
SAR ADC ENCODER
CONVERSION TIME = 8µs
N
fs = 100 kSPS
EXAMPLE:
dv = 1 LSB = q
dt = 8µs
N = 12, 2N = 4096
fmax = 9.7 Hz
qπ 2N
Figure 5.24: Input Frequency Limitations of Nonsampling ADC (Encoder)
Most ADCs today have a built-in sample-and-hold function (SHA), thereby allowing
them to process ac signals. This type of ADC is referred to as a sampling ADC. However,
many early ADCs, such as the Analog Devices’ industry-standard AD574, were not of
the sampling type, but simply encoders as shown in Figure 5.24. If the input signal to a
SAR ADC (assuming no SHA function) changes by more than 1 LSB during the
conversion time (8 µs in the example), the output data can have large errors, depending
on the location of the code. Most ADC architectures are subject to this type of error—
5.22
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
SAMPLING THEORY
some more, some less—with the possible exception of flash converters having wellmatched comparators.
Assume that the input signal to the encoder is a sine wave with a full-scale amplitude
(q2N/2), where q is the weight of 1 LSB.
v(t) = q (2N/2) sin (2π f t).
Eq. 5.1
dv/dt = q 2πf (2N/2) cos (2π f t).
Eq. 5.2
Taking the derivative:
The maximum rate of change is therefore:
dv/dt |max = q 2πf (2N/2).
Eq. 5.3
f = (dv/dt |max )/(q π 2N).
Eq. 5.4
Solving for f:
If N = 12, and 1 LSB change (dv = q) is allowed during the conversion time (dt = 8 µs),
then the equation can be solved for fmax, the maximum full-scale signal frequency that can
be processed without error:
fmax = 9.7 Hz.
This implies any input frequency greater than 9.7 Hz is subject to conversion errors, even
though a sampling frequency of 100 kSPS is possible with the 8 µs ADC (this allows an
extra 2 µs interval for an external SHA to re-acquire the signal after coming out of the
hold mode).
To process ac signals, a sample-and-hold function is added. The ideal SHA is simply a
switch driving a hold capacitor followed by a high input impedance buffer. The input
impedance of the buffer must be high enough so that the capacitor is discharged by less
than 1 LSB during the hold time. The SHA samples the signal in the sample mode, and
holds the signal constant during the hold mode. The timing is adjusted so that the encoder
performs the conversion during the hold time. A sampling ADC can therefore process
fast signals—the upper frequency limitation is determined by the SHA aperture jitter,
bandwidth, distortion, etc., not the encoder. In the example shown, a good sample-andhold could acquire the signal in 2 µs, allowing a sampling frequency of 100 kSPS, and
the capability of processing input frequencies up to 50 kSPS. A complete discussion of
the SHA function including these specifications follows later in this chapter.
5.23
BASIC LINEAR DESIGN
The Nyquist Criteria
A continuous analog signal is sampled at discrete intervals, ts = 1/fs, which must be
carefully chosen to ensure an accurate representation of the original analog signal. It is
clear that the more samples taken (faster sampling rates), the more accurate the digital
representation, but if fewer samples are taken (lower sampling rates), a point is reached
where critical information about the signal is actually lost. The mathematical basic of
sampling was set forth by Harry Nyquist of Bell Telephone Laboratories in two classic
papers published in 1924 and 1928, respectively. (See References 1 and 2). Nyquist's
original work was shortly supplemented by R. V. L. Hartley (Reference 3). These papers
formed the basis for the PCM work to follow in the 1940s, and in 1948, Claude Shannon
wrote his classic paper on communication theory (Reference 4).
Simply stated, the Nyquist criteria require that the sampling frequency be at least twice
the highest frequency contained in the signal, or information about the signal will be lost.
If the sampling frequency is less than twice the maximum analog signal frequency, a
phenomenon known as aliasing will occur.
‹A
signal with a maximum BANDWIDTH fa must be sampled at a rate fa
> 2 fa or information about the signal will be lost because of aliasing
‹ Aliasing
occurs whenever fa < 2 fa
‹ The
concept of aliasing is widely used I communications applications
such as direct IF-to-digital conversion
‹A
signal which has frequencyncomponents between fa and fb must be
sampled at least at a rate fa > 2 (fb – fa) to prevent alias components
from overlapping the signal frequncies.
Figure 5.25: Nyquist’s Criteria
In order to understand the implications of aliasing in both the time and frequency
domain, first consider case of a time domain representation of a single tone sine wave
sampled as shown in Figure 5.26. In this example, the sampling frequency fs is not at
least 2fa, but only slightly more than the analog input frequency fa—the Nyquist criteria is
violated. Notice that the pattern of the actual samples produces an aliased sine wave at a
lower frequency equal to fs – fa.
The corresponding frequency domain representation of this scenario is shown in Figure
5.27B. Now consider the case of a single frequency sine wave of frequency fa sampled at
a frequency fs by an ideal impulse sampler (see Figure 5.27A). Also assume that fs > 2fa
as shown. The frequency-domain output of the sampler shows aliases or images of the
original signal around every multiple of fs, i.e. at frequencies equal to |± Kfs ± fa|, K = 1,
2, 3, 4, ...
5.24
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
SAMPLING THEORY
ALIASED SIGNAL = fs – fa
INPUT = fa
1
fs
t
NOTE: fa IS SLIGHTLY LESS THAN fs
Figure 5.26: Aliasing in the Time Domain
A
fa
fs
0.5fs
1st NYQUIST
ZONE
B
I
I
2nd NYQUIST
ZONE
0.5fs
4th NYQUIST
ZONE
I
fs
2fs
1.5fs
3rd NYQUIST
ZONE
fa
I
I
I
I
I
1.5fs
2fs
Figure 5.27: Analog Signal fa Sampled @ fs Using Ideal Sampler
Has Images (Aliases) at |± Kfs ± fa|, K = 1, 2, 3, …
The Nyquist bandwidth is defined to be the frequency spectrum from dc to fs/2. The
frequency spectrum is divided into an infinite number of Nyquist zones, each having a
width equal to 0.5 fs as shown. In practice, the ideal sampler is replaced by an ADC
followed by an FFT processor. The FFT processor only provides an output from dc to
fs/2, i.e., the signals or aliases which appear in the first Nyquist zone.
5.25
BASIC LINEAR DESIGN
Now consider the case of a signal which is outside the first Nyquist zone (Figure 5.27B).
The signal frequency is only slightly less than the sampling frequency, corresponding to
the condition shown in the time domain representation in Figure 5.26. Notice that even
though the signal is outside the first Nyquist zone, its image (or alias), fs – fa, falls inside.
Returning to Figure 5.27A, it is clear that if an unwanted signal appears at any of the
image frequencies of fa, it will also occur at fa, thereby producing a spurious frequency
component in the first Nyquist zone.
This is similar to the analog mixing process and implies that some filtering ahead of the
sampler (or ADC) is required to remove frequency components which are outside the
Nyquist bandwidth, but whose aliased components fall inside it. The filter performance
will depend on how close the out-of-band signal is to fs/2 and the amount of attenuation
required.
Baseband Antialiasing Filters
Baseband sampling implies that the signal to be sampled lies in the first Nyquist zone. It
is important to note that with no input filtering at the input of the ideal sampler, any
frequency component (either signal or noise) that falls outside the Nyquist bandwidth in
any Nyquist zone will be aliased back into the first Nyquist zone. For this reason, an
antialiasing filter is used in almost all sampling ADC applications to remove these
unwanted signals.
Properly specifying the antialiasing filter is important. The first step is to know the
characteristics of the signal being sampled. Assume that the highest frequency of interest
is fa. The antialiasing filter passes signals from dc to fa while attenuating signals above fa.
Assume that the corner frequency of the filter is chosen to be equal to fa. The effect of the
finite transition from minimum to maximum attenuation on system dynamic range is
illustrated in Figure 5.28A.
Assume that the input signal has full-scale components well above the maximum
frequency of interest, fa. The diagram shows how full-scale frequency components above
fs – fa are aliased back into the bandwidth dc to fa. These aliased components are
indistinguishable from actual signals and therefore limit the dynamic range to the value
on the diagram which is shown as DR.
Some texts recommend specifying the antialiasing filter with respect to the Nyquist
frequency, fs/2, but this assumes that the signal bandwidth of interest extends from dc to
fs/2 which is rarely the case. In the example shown in Figure 5.28A, the aliased
components between fa and fs/2 are not of interest and do not limit the dynamic range.
The antialiasing filter transition band is therefore determined by the corner frequency fa,
the stopband frequency fs – fa, and the desired stopband attenuation, DR. The required
system dynamic range is chosen based on the requirement for signal fidelity.
5.26
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
SAMPLING THEORY
fa
B
A
fa
fs - f a
Kfs - fa
DR
fs
2
fs
STOPBAND ATTENUATION = DR
Kfs
2
STOPBAND ATTENUATION = DR
TRANSITION BAND: fa to fs - fa
CORNER FREQUENCY: fa
TRANSITION BAND: fa to Kfs - fa
CORNER FREQUENCY: fa
Kfs
Figure 5.28: Oversampling Relaxes Requirements
on Baseband Antialiasing Filter
Filters become more complex as the transition band becomes sharper, all other things
being equal. For instance, a Butterworth filter gives 6 dB attenuation per octave for each
filter pole. Achieving 60 dB attenuation in a transition region between 1 MHz and 2 MHz
(1 octave) requires a minimum of 10 poles—not a trivial filter, and definitely a design
challenge.
Therefore, other filter types are generally more suited to high speed applications where
the requirement is for a sharp transition band and in-band flatness coupled with linear
phase response. Elliptic filters meet these criteria and are a popular choice. There are a
number of companies which specialize in supplying custom analog filters. TTE is an
example of such a company (Reference 5).
From this discussion, we can see how the sharpness of the antialiasing transition band can
be traded off against the ADC sampling frequency. Choosing a higher sampling rate
(oversampling) reduces the requirement on transition band sharpness (hence, the filter
complexity) at the expense of using a faster ADC and processing data at a faster rate.
This is illustrated in Figure 5.28B which shows the effects of increasing the sampling
frequency by a factor of K, while maintaining the same analog corner frequency, fa, and
the same dynamic range, DR, requirement. The wider transition band (fa to Kfs – fa)
makes this filter easier to design than for the case of Figure 5.28A.
The antialiasing filter design process is started by choosing an initial sampling rate of 2.5
to 4 times fa. Determine the filter specifications based on the required dynamic range and
see if such a filter is realizable within the constraints of the system cost and performance.
If not, consider a higher sampling rate which may require using a faster ADC. It should
be mentioned that sigma-delta ADCs are inherently oversampling converters, and the
5.27
BASIC LINEAR DESIGN
resulting relaxation in the analog antialiasing filter requirements is therefore an added
benefit of this architecture.
The antialiasing filter requirements can also be relaxed somewhat if it is certain that there
will never be a full-scale signal at the stopband frequency fs – fa. In many applications, it
is improbable that full-scale signals will occur at this frequency. If the maximum signal at
the frequency fs – fa will never exceed X dB below full-scale, then the filter stopband
attenuation requirement is reduced by that same amount. The new requirement for
stopband attenuation at fs – fa based on this knowledge of the signal is now only DR –
X dB. When making this type of assumption, be careful to treat any noise signals which
may occur above the maximum signal frequency fa as unwanted signals which will also
alias back into the signal bandwidth.
Undersampling
Thus far we have considered the case of baseband sampling, i.e., all the signals of interest
lie within the first Nyquist zone. Figure 5.29A shows such a case, where the band of
sampled signals is limited to the first Nyquist zone, and images of the original band of
frequencies appear in each of the other Nyquist zones.
A
ZONE 1
I
I
0.5fs
fs
I
I
1.5fs
I
I
2.5fs
2fs
3.5fs
3fs
ZONE 2
B
I
I
fs
0.5fs
I
1.5fs
I
I
2fs
I
3.5fs
3fs
2.5fs
ZONE 3
C
I
I
0.5fs
I
fs
1.5fs
I
2fs
I
2.5fs
I
3fs
3.5fs
Figure 5.29: Undersampling and Frequency Translation Between Nyquist Zones
Consider the case shown in Figure 5.29B, where the sampled signal band lies entirely
within the second Nyquist zone. The process of sampling a signal outside the first
Nyquist zone is often referred to as undersampling, or harmonic sampling (also referred
to as band-pass sampling, IF sampling, direct IF to digital conversion). Note that the first
Nyquist zone image contains all the information in the original signal, with the exception
of its original location (the order of the frequency components within the spectrum is
reversed, but this is easily corrected by re-ordering the output of the FFT).
5.28
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
SAMPLING THEORY
Figure 5.29C shows the sampled signal restricted to the third Nyquist zone. Note that the
first Nyquist zone image has no frequency reversal. In fact, the sampled signal
frequencies may lie in any unique Nyquist zone, and the first Nyquist zone image is still
an accurate representation (with the exception of the frequency reversal which occurs
when the signals are located in even Nyquist zones). At this point we can clearly restate
the Nyquist criteria:
A signal must be sampled at a rate equal to or greater than twice its bandwidth in order
to preserve all the signal information.
Notice that there is no mention of the precise location of the band of sampled signals
within the frequency spectrum relative to the sampling frequency. The only constraint is
that the band of sampled signals be restricted to a single Nyquist zone, i.e., the signals
must not overlap any multiple of fs/2 (this, in fact, is the primary function of the
antialiasing filter).
Sampling signals above the first Nyquist zone has become popular in communications
because the process is equivalent to analog demodulation. It is becoming common
practice to sample IF signals directly and then use digital techniques to process the signal,
thereby eliminating the need for the IF demodulator and filters. Clearly, however, as the
IF frequencies become higher, the dynamic performance requirements on the ADC
become more critical. The ADC input bandwidth and distortion performance must be
adequate at the IF frequency, rather than only baseband. This presents a problem for most
ADCs designed to process signals in the first Nyquist zone, therefore, an ADC suitable
for undersampling applications must maintain dynamic performance into the higher order
Nyquist zones.
Antialiasing Filters in Undersampling Applications
Figure 5.30 shows a signal in the second Nyquist zone centered around a carrier
frequency, fc, whose lower and upper frequencies are f1 and f2. The antialiasing filter is a
band-pass filter. The desired dynamic range is DR, which defines the filter stopband
attenuation. The upper transition band is f2 to 2fs – f2, and the lower is f1 to fs – f1. As in
the case of baseband sampling, the antialiasing filter requirements can be relaxed by
proportionally increasing the sampling frequency, but fc must also be increased so that it
is always centered in the second Nyquist zone.
Two key equations can be used to select the sampling frequency, fs, given the carrier
frequency, fc, and the bandwidth of its signal, Δf. The first is the Nyquist criteria:
fs > 2Δf .
Eq. 5.5
The second equation ensures that fc is placed in the center of a Nyquist zone:
fs =
4f c
,
2 NZ − 1
Eq. 5.6
5.29
BASIC LINEAR DESIGN
where NZ = 1, 2, 3, 4, .... and NZ corresponds to the Nyquist zone in which the carrier
and its signal fall (see Figure 5.36).
fs - f1
f1
f2
2fs - f 2
fc
DR
SIGNALS
OF
INTEREST
IMAGE
0
0.5fS
IMAGE
IMAGE
fS
BANDPASS FILTER SPECIFICATIONS:
1.5fS
2fS
STOPBAND ATTENUATION = DR
TRANSITION BAND: f2 TO 2f s - f2
f1 TO f s - f1
CORNER FREQUENCIES: f1, f2
Figure 5.30: Antialiasing Filter for Undersampling
NZ is normally chosen to be as large as possible while still maintaining fs > 2Δf. This
results in the minimum required sampling rate. If NZ is chosen to be odd, then fc and its
signal will fall in an odd Nyquist zone, and the image frequencies in the first Nyquist
zone will not be reversed. Trade-offs can be made between the sampling frequency and
the complexity of the antialiasing filter by choosing smaller values of NZ (hence a higher
sampling frequency).
5.30
FUNDAMENTALS OF SAMPLED DATA SYSTEMS
SAMPLING THEORY
ZONE NZ - 1
ZONE NZ
ZONE NZ + 1
I
Δf
I
fc
0.5fs
fs > 2 Δ f
0.5fs
0.5fs
fs =
4fc
2NZ - 1
, NZ = 1, 2, 3, . . .
Figure 5.31: Centering an Undersampled Signal within a Nyquist Zone
As an example, consider a 4 MHz wide signal centered around a carrier frequency of
71 MHz. The minimum required sampling frequency is therefore 8 MSPS. Solving
Eq. 5.6 for NZ using fc = 71 MHz and fs = 8 MSPS yields NZ = 18.25. However, NZ
must be an integer, so we round 18.25 to the next lowest integer, 18. Solving Eq. 5.6
again for fs yields fs = 8.1143 MSPS. The final values are therefore fs = 8.1143 MSPS,
fc = 71 MHz, and NZ = 18.
Now assume that we desire more margin for the antialiasing filter, and we select fs to be
10 MSPS. Solving Eq. 5.6 for NZ, using fc = 71 MHz and fs = 10 MSPS yields NZ =
14.7. We round 14.7 to the next lowest integer, giving NZ = 14. Solving Eq. 5.6 again for
fs yields fs = 10.519 MSPS. The final values are therefore fs = 10.519 MSPS, fc =
71 MHz, and NZ = 14.
The above iterative process can also be carried out starting with fs and adjusting the
carrier frequency to yield an integer number for NZ.
5.31
BASIC LINEAR DESIGN
REFERENCES:
SAMPLING THEORY
1.
H. Nyquist, “Certain Factors Affecting Telegraph Speed,” Bell System Technical Journal, Vol. 3,
April 1924, pp. 324-346.
2.
H. Nyquist, “Certain Topics in Telegraph Transmission Theory,” A.I.E.E. Transactions, Vol. 47,
April 1928, pp. 617-644.
3.
R.V.L. Hartley, “Transmission of Information,” Bell System Technical Journal, Vol. 7, July 1928,
pp. 535-563.
4.
C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal,
Vol. 27, July 1948, pp. 379-423 and October 1948, pp. 623-656.
5.
TTE, Inc., 11652 Olympic Blvd., Los Angeles, CA 90064, http://www.tte.com
5.32
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertising