This document summarizes key figures of merit for testing analog-to-digital converters. It describes static parameters like resolution, accuracy, and nonlinearity errors. It also covers frequency-domain dynamic parameters including signal-to-noise ratio, spurious-free dynamic range, total harmonic distortion, and intermodulation distortion. Time-domain dynamic parameters like aperture delay and jitter are also mentioned. Key concepts are explained through examples and diagrams.
UNIT III BASEBAND TRANSMISSION
Properties of Line codes- Power Spectral Density of Unipolar / Polar RZ & NRZ ā Bipolar NRZ - Manchester- ISI ā Nyquist criterion for distortionless transmission ā Pulse shaping ā Correlative coding - Mary schemes ā Eye pattern ā Equalization
UNIT III BASEBAND TRANSMISSION
Properties of Line codes- Power Spectral Density of Unipolar / Polar RZ & NRZ ā Bipolar NRZ - Manchester- ISI ā Nyquist criterion for distortionless transmission ā Pulse shaping ā Correlative coding - Mary schemes ā Eye pattern ā Equalization
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47296653 testing-adc
1. Analog-to-Digital Converter Testing
Kent H. Lundberg
Analog-to-digital converters are essential building blocks in modern electronic systems. They
form the critical link between front-end analog transducers and back-end digital computers that
can eļ¬ciently implement a wide variety of signal-processing functions. The wide variety of digital-
signal-processing applications leads to the availability of a wide variety of analog-to-digital (A/D)
converters of varying price, performance, and quality.
Ideally, an A/D converter encodes a continuous-time analog input voltage, VIN , into a series of
discrete N-bit digital words that satisfy the relation
VIN = VFS
Nā1
k=0
bk
2k+1
+
where VFS is the full-scale voltage, bk are the individual output bits, and is the quantization error.
This relation can also be written in terms of the least signiļ¬cant bit (LSB) or quantum voltage
level
VQ =
VFS
2N
= 1 LSB
as
VIN = VQ
Nā1
k=0
bk2k
+
A plot of this ideal characteristic for a 3-bit A/D converter is shown in Figure 1.
111
110
101
100
011
010
001
000
DigitalOutput
Analog Input
1/80 1/4 3/8 1/2 5/8 3/4 7/8 FS
center
code
= 1 LSB
code width
Figure 1: Ideal analog-to-digital converter characteristic.
1
2. Each unique digital code corresponds to a small range of analog input voltages. This range is
1 LSB wide (the ācode widthā) and is centered around the ācode center.ā All input voltages resolve
to the digital code of the nearest code center. The diļ¬erence between the analog input voltage and
the corresponding voltage of the nearest code center (the diļ¬erence between the solid and dashed
lines in Figure 1) is the quantization error. Since the A/D converter has a ļ¬nite number of output
bits, even an ideal A/D converter produces some quantization error with every sample.
2
3. 1 A/D Converter Figures of Merit
The number of output bits from an analog-to-digital converter do not fully specify its behavior.
Real A/D converters can diļ¬er from ideal behavior in many ways. While static imperfections,
such as gain and oļ¬set, are easy to quantify, the success of many signal-processing applications
depends on the dynamic behavior of the A/D converter. Ultimately, the application determines
the requirements, and A/D converter resolution may not be either necessary or suļ¬cient to specify
the required performance. In many cases, the quality of the A/D converter must be tested for the
speciļ¬c application.
The wide variety of analog-to-digital converter applications leads to a large number of ļ¬gures
of merit for specifying performance. These ļ¬gures of merit include accuracy, resolution, dynamic
range, oļ¬set, gain, diļ¬erential nonlinearity, integral nonlinearity, signal-to-noise ratio, signal-to-
noise-and-distortion ratio, eļ¬ective number of bits, spurious-free dynamic range, intermodulation
distortion, total harmonic distortion, eļ¬ective resolution bandwidth, full-power bandwidth, full-
linear bandwidth, aperture delay, aperture jitter, transient response, and overvoltage recovery.
These speciļ¬cations can be loosely divided into three categories ā static parameters, frequency-
domain dynamic parameters, and time-domain dynamic parameters ā and are deļ¬ned in this
section.
1.1 Static Parameters
Static parameters are the A/D converter speciļ¬cations that can be tested at low speed, or even
with constant voltages. These speciļ¬cations include accuracy, resolution, dynamic range, oļ¬set,
gain, diļ¬erential nonlinearity, and integral nonlinearity.
1.1.1 Accuracy
Accuracy is the total error with which the A/D converter can convert a known voltage, including
the eļ¬ects of quantization error, gain error, oļ¬set error, and nonlinearities. Technically, accuracy
should be traceable to known standards (for example, NIST), and is generally a ācatch-allā term
for all static errors.
1.1.2 Resolution
Resolution is the number of bits, N, out of the A/D converter. The characteristic in Figure 1
shows a 3-bit A/D converter. Probably the most noticeable speciļ¬cation, resolution determines the
size of the least signiļ¬cant bit, and thus determines the dynamic range, the code widths, and the
quantization error.
1.1.3 Dynamic Range
Dynamic range is the ratio of the smallest possible output (the least signiļ¬cant bit or quantum
voltage) to the largest possible output (full-scale voltage), mathematically 20 log10 2N ā 6N.
1.1.4 Oļ¬set Error
Oļ¬set error is the deviation in the A/D converterās behavior at zero. The ļ¬rst transition voltage
should be 1/2 LSB above analog ground. Oļ¬set error is the deviation of the actual transition voltage
from the ideal 1/2 LSB. Oļ¬set error is easily trimmed by calibration. Compare the location of the
ļ¬rst transitions in Figures 1 and 2.
3
4. 111
110
101
100
011
010
001
000
DigitalOutput
Analog Input
0 1/8 1/4 3/8 1/2 5/8 3/4 7/8 FS
Figure 2: Analog-to-digital converter characteristic, showing oļ¬set and gain error.
1.1.5 Gain Error
Gain error is the deviation in the slope of the line through the A/D converterās end points at zero
and full scale from the ideal slope of 2N /VFS codes-per-volt. Like oļ¬set error, gain error is easily
corrected by calibration. Compare the slope of the dashed lines in Figures 1 and 2.
110
101
100
011
010
001
000
0
111
DigitalOutput
Analog Input
maximum
1/8 1/4 3/8 1/2 5/8 3/4 7/8 FS
narrow code widths
negative DNL
wide code widths
positive DNL
missing code
INL = 1 LSB
Figure 3: Analog-to-digital converter characteristic, showing nonlinearity errors and a missing code.
The dashed line is the ideal characteristic, and the dotted line is the best ļ¬t.
1.1.6 Diļ¬erential Nonlinearity
Diļ¬erential nonlinearity (DNL) is the deviation of the code transition widths from the ideal width
of 1 LSB. All code widths in the ideal A/D converter are 1 LSB wide, so the DNL would be zero
4
5. everywhere. Some datasheets list only the āmaximum DNL.ā See the wide range of code widths
illustrated in Figure 3.
1.1.7 Integral Nonlinearity
Integral nonlinearity (INL) is the distance of the code centers in the A/D converter characteristic
from the ideal line. If all code centers land on the ideal line, the INL is zero everywhere. Some
datasheets list only the āmaximum INL.ā See the deviations of the code centers from the ideal line
in Figure 3.
Note that there are two possible ways to express maximum INL, depending on your deļ¬nition
of the āideal line.ā Datasheet INL numbers can be decreased by quoting maximum INL from a
ābest ļ¬tā line instead of the ideal line. In Figure 3, the ideal line (shown dashed) exhibits an
INL of 1 LSB, while the best-ļ¬t line (shown dotted) exhibits an INL of half that size. While this
prevarication underestimates the eļ¬ect of INL on total accuracy, it is probably a better reļ¬ection
of the A/D converterās linearity in AC-coupled applications.
1.1.8 Missing Codes
Missing codes are output digital codes that are not produced for any input voltage, usually due to
large DNL. In some converters, missing codes can be caused by non-monotonicity of the internal
D/A. The large DNL in Figure 3 causes code 100 to be ācrowded out.ā
1.2 Resolution and Quantization Noise
Quantization error due to the ļ¬nite resolution N of the A/D converter limits the signal-to-noise
ratio. Even in an ideal A/D converter, the quantization of the input signal creates errors that
behave like noise. All inputs within Ā±1/2 LSB of a code center resolve to that digital code. Thus,
there will be a small diļ¬erence between the code center and the actual input voltage due to this
quantization. If assume that this error voltage is uncorrelated and distributed uniformly, we can
calculate the expected rms value of this āquantization noise.ā
111
110
101
100
011
010
001
000
DigitalOutput
Analog Input
1/80 1/4 3/8 1/2 5/8 3/4 7/8 FS
center
code
= 1 LSB
code width
0 1/8 1/4 3/8 1/2 5/8 3/4 7/8 FS
Analog Input
ErrorVoltage
Error = Output ā Input
Figure 4: Quantization error voltage for ideal analog-to-digital converter.
The signal-to-noise ratio due to quantization can be directly calculated. The range of the error
5
6. voltage is the quantum voltage level (the least signiļ¬cant bit)
VQ =
VFS
2N
= 1 LSB
Finite amplitude resolution introduces a quantization error between the analog input voltage and
the reconstructed output voltage. Assuming this quantization error voltage is uniformly distributed
over the code width from ā1/2 LSB to +1/2 LSB, the expectation value of the error voltage is
E{ 2
} =
1
VQ
+ 1
2
VQ
ā 1
2
VQ
2
d =
1
VQ
3
3
+ 1
2
VQ
ā 1
2
VQ
=
V 2
Q
12
This quantization noise is assumed to be uncorrelated and broadband. Using this result, the
maximum signal-to-noise ratio for a full-scale input can be calculated. The rms value of a full-scale
peak-to-peak amplitude VF is
Vrms =
VFS
2
ā
2
=
2N VQ
2
ā
2
thus the signal-to-noise ratio is
SNR = 20 log
Vrms
E( 2)
= 20 log(2N
ā
1.5) = 6.02N + 1.76 dB
when the noise is due only to quantization. Using this result, we can tabulate the ideal signal-to-
noise ratio for several A/D converter resolutions in Figure 5.
resolution signal-to-noise ratio
6 bits 37.9 dB
8 bits 49.9 dB
10 bits 62.0 dB
12 bits 74.0 dB
14 bits 86.0 dB
16 bits 98.1 dB
Figure 5: Ideal signal-to-noise ratio due to quantization versus resolution.
Figure 6 shows the fast Fourier transform (FFT) spectrum of a sine wave sampled by an ideal
10-bit A/D converter. The noise ļ¬oor in this ļ¬gure is due only to quantization noise. For an
M-point FFT, the average value of the noise contained in each frequency bin is 10 log2(M/2) dB
below the rms value of the quantization noise. This āFFT process gainā is why the noise ļ¬oor
appears 30 dB lower than the quantization noise level listed in Figure 5.
1.3 Frequency-Domain Dynamic Parameters
All real analog-to-digital converters have additional noise sources and distortion processes that
degrade the performance of the A/D converter from the ideal signal-to-noise ratio calculated above.
These imperfections in the dynamic behavior of the A/D converter are quantiļ¬ed and reported in
a variety of ways.
6
7. 1e-05
0.0001
0.001
0.01
0.1
1
0 500 1000 1500 2000
normalizedamplitude
FFT frequency bin
Figure 6: Quantization noise ļ¬oor for an ideal 10-bit A/D converter (4096 point FFT).
1.3.1 Signal-to-Noise-and-Distortion Ratio
Signal-to-noise-and-distortion ratio (S/N+D, SINAD, or SNDR) is the ratio of the input signal
amplitude to the rms sum of all other spectral components. For an M-point FFT of a sine wave
test, if the fundamental is in frequency bin m (with amplitude Am), the SNDR can be calculated
from the FFT amplitudes
SNDR = 10 log
ļ£®
ļ£Æ
ļ£°A2
m
ļ£«
ļ£
mā1
k=1
A2
k +
M/2
k=m+1
A2
k
ļ£¶
ļ£ø
ā1
ļ£¹
ļ£ŗ
ļ£»
To avoid any spectral leakage around the fundamental, often several bins around the fundamental
are ignored. The SNDR is dependent on the input-signal frequency and amplitude, degrading at
high frequency and power. Measured results are often presented in plots of SNDR versus frequency
for a constant-amplitude input, or SNDR versus amplitude for a constant-frequency input.
1.3.2 Eļ¬ective Number of Bits
Eļ¬ective number of bits (ENOB) is simply the signal-to-noise-and-distortion ratio expressed in bits
rather than decibels by solving the āideal SNRā equation
SNR = 6.02N + 1.76 dB
for the number of bits N, using the measured SNDR
ENOB =
SNDR ā 1.76 dB
6.02 dB/bit
In the presentation of measured results, ENOB is identical to SNDR, with a change in the scaling
of the vertical axis.
1.3.3 Spurious-Free Dynamic Range
Spurious-free dynamic range (SFDR) is the ratio of the input signal to the peak spurious or peak
harmonic component. Spurs can be created at harmonics of the input frequency due to nonlineari-
ties in the A/D converter, or at subharmonics of the sampling frequency due to mismatch or clock
7
8. coupling in the circuit. The SFDR of an A/D converter can be larger than the SNDR. Measurement
of SFDR can be facilitated by increasing the number of FFT points or by averaging several data
sets. In both cases, the noise ļ¬oor will improve, while the amplitude of the spurs will stay constant.
The spectrum of an A/D converter with signiļ¬cant harmonic spurs is shown in Figure 7. Because
SFDR is often slew-rate dependent, it will be a function of input frequency and magnitude [1]. The
maximum SFDR often occurs at an amplitude below full scale.
1e-05
0.0001
0.001
0.01
0.1
1
0 500 1000 1500 2000
normalizedamplitude
FFT frequency bin
Figure 7: A/D converter with signiļ¬cant nonlinearity, showing poor SFDR and THD. Note that
high frequency harmonics of the input signal are aliased down to frequencies below the Nyquist
frequency.
1.3.4 Total Harmonic Distortion
Total harmonic distortion (THD) is the ratio of the rms sum of the ļ¬rst ļ¬ve harmonic components
(or their aliased versions, as in Figure 7) to the input signal
THD = 10 log
V 2
2 + V 2
3 + V 2
4 + V 2
5 + V 2
6
V 2
1
where V1 is the amplitude of the fundamental, and Vn is the amplitude of the n-th harmonic.
1.3.5 Intermodulation Distortion
Intermodulation distortion (IMD) is the ratio of the amplitudes of the sum and diļ¬erence frequencies
to the input signals for a two-tone test, sometimes expressed as āintermod-free dynamic range
(IFDR)ā See the FFT spectrum in Figure 8. For second-order distortion, the IMD would be
IMD = 10 log
V 2
+ + V 2
ā
V 2
1 + V 2
2
where V1 and V2 are the rms amplitudes of the input signals, and V+ and Vā are the rms amplitudes
of the sum and diļ¬erence intermodulation products. See Figure 8.
1.3.6 Eļ¬ective Resolution Bandwidth
Eļ¬ective resolution bandwidth (ERBW) is the input-signal frequency where the SNDR of the A/D
converter has fallen by 3 dB (0.5 bit) from its value for low-frequency input signals.
8
9. 1e-05
0.0001
0.001
0.01
0.1
1
0 500 1000 1500 2000
normalizedamplitude
FFT frequency bin
Figure 8: Two-tone IMD test with second-order nonlinearity, showing (from left) F2 ā F1 product,
F1 input, F2 input, 2F1 product, F1 + F2 product, and 2F2 product.
1.3.7 Full-Power Bandwidth
In ampliļ¬ers, full-power bandwidth (FPBW) is the maximum frequency at which the ampliļ¬er
can reproduce a full-scale sinusoidal output without distortion (sometimes calculated as slew-rate
divided by Ā±2ĻVmax), or where the amplitude of full-scale sinusoid is reduced by 3 dB. Using this
deļ¬nition for A/D converters can result in optimistic frequencies where the SNDR is severely de-
graded. Some manufacturer report the full-power bandwidth as the frequency where the amplitude
of the reconstructed input signal is reduced by 3 dB [2].
1.3.8 Full-Linear Bandwidth
Full-linear bandwidth is the frequency where the slew-rate limit of the input sample-and-hold begins
distorting the input signal by some speciļ¬ed amount ([3] uses 0.1 dB)
1.4 Time-Domain Dynamic Parameters
1.4.1 Aperture Delay
Aperture delay is the delay from when the A/D converter is triggered (perhaps the rising edge of
the sampling clock) to when it actually converts the input voltage into the appropriate digital code.
Aperture delay is also sometimes called aperture time.
1.4.2 Aperture Jitter
Aperture jitter is the sample-to-sample variation in the aperture delay. The rms voltage error
caused by rms aperture jitter decreases the overall signal-to-noise ratio, and is a signiļ¬cant limiting
factor in the performance of high-speed A/D converters [4].
If we assume that the input waveform is a sinusoid
VIN = VFS sin Ļt
then the maximum slope of the input waveform is
dVIN
dt max
= ĻVFS
9
10. ā
āv
t
1 2 5 502010 100
90
80
70
60
50
40
30
20
Input Frequency(MHz)
SJNR(dB)
2 ps
10 ps
50 ps
250 ps
Figure 9: Eļ¬ects of aperture jitter.
which occurs at the zero crossings. If there is an rms error in the time at which we sample (aperture
jitter, ta) during this maximum slope, then there will be an rms voltage error of
Vrms = ĻVFSta = 2ĻfVFSta
Since the aperture time variations are random, these voltage errors will behave like a random noise
source. Thus the signal-to-jitter-noise ratio
SJNR = 20 log
VFS
Vrms
= 20 log
1
2Ļfta
The SJNR for several values of the jitter ta is shown in Figure 9.
1.4.3 Transient Response
Transient response is the settling time for the A/D converter to full accuracy (to within Ā±1/2 LSB)
after a step in input voltage from zero to full scale
1.4.4 Overvoltage Recovery
Overvoltage recovery is the settling time for the A/D converter to full accuracy after a step in input
voltage from outside the full scale voltage (for example, from 1.5VF to 0.5VF )
10
11. 2 Dynamic A/D Converter Testing Methods
A wide variety of tests have been developed to measure dynamic speciļ¬cations. Many of these tests
rely on Fourier analysis using the discrete Fourier transform (DFT) and the fast Fourier transform
(FFT), as well as other mathematical models.
2.1 Testing with the Fast Fourier Transform
The simplest frequency-domain tests use the direct application of the fast Fourier transform. Taking
the FFT of the output data while driving the A/D converter with a single, low distortion sine
wave, the SNDR, ENOB, SFDR, and THD can easily be calculated. It is useful to take these
measurements at several input amplitudes and frequencies, and plot the results. Taking data for
high input frequencies allows the full-power and full-linear bandwidths to be calculated.
Two more tests are completed while driving the A/D converter with an input composed of two
sine waves of diļ¬erent frequencies. The FFT of this test result is used to calculate the IMD (for
second-order and third-order products) and the two-tone SFDR.
2.2 Signal Coherence for FFT Tests
One potential problem in using Fourier analysis is solved by paying close attention to the coherence
of the sampled waveform in the data record. Unless the sampled data contains a whole number
of periods of the input waveform, spectral leakage of the input frequency can obscure the results
[5]. Figure 10 shows the FFT of a 4096-point data record containing 127.5 periods of a perfect sine
wave. This spectrum should contain a single impulse in frequency, but the incomplete cycle of the
sine wave at the end of the data record causes the spectrum to be broadly smeared.1
1e-05
0.0001
0.001
0.01
0.1
1
0 500 1000 1500 2000
normalizedamplitude
FFT frequency bin
Figure 10: FFT of non-integer number of cycles (4096 points, 127.5 cycles).
In addition, the number of periods of the input waveform in the sample record should not be
a non-prime integer sub-multiple of the record length. For example, using a power-of-two for both
the number of periods and the number of samples results in repetitive data. Figure 11 shows the
FFT of a 4096-point data record that contains 128 periods of a sine wave. In this case, the ļ¬rst
32 samples of the data record are simply repeated 128 times. This input vector does a poor job
of exercising the device under test, and the quantization noise is concentrated in the harmonics of
the input frequency rather than uniformly distributed across the Nyquist bandwidth [7].
1
This smearing can be solved by windowing [6], but it is easier to simply use a whole number of cycles.
11
12. 1e-05
0.0001
0.001
0.01
0.1
1
0 500 1000 1500 2000
normalizedamplitude
FFT frequency bin
Figure 11: FFT of even divisor number of cycles (4096 points, 128 cycles).
Figure 12 shows the FFT of a 4096-point data record that contains 127 periods of a perfect
sine wave. Since 127 is both odd and prime, there are no common factors between the number of
input periods and the number of samples in the data record. Because of these choices, there are no
repeating patterns and every sample in the data record is unique. This FFT correctly shows the
frequency impulse due to the input sine wave and the noise ļ¬oor due to quantization, without any
measurement-induced artifacts.
1e-05
0.0001
0.001
0.01
0.1
1
0 500 1000 1500 2000
normalizedamplitude
FFT frequency bin
Figure 12: FFT of non-divisor prime number of cycles (4096 points, 127 cycles).
12
13. 2.3 Histogram Test for Linearity
The linearity (INL and DNL) of the A/D converter can be determined with a histogram test [8, 9].
A histogram of output digital codes is recorded for a large number of samples for an input sine
wave. The results are compared to the number of samples expected from the theoretical sine-wave
probability-density function. If the input sine wave is
V = A sin Ļt
then the probability-density function is
p(V ) =
1
Ļ
ā
A2 ā V 2
.
The probability of a sample being in the range (Va, Vb) is found by integration
P(Va, Vb) =
Va
Vb
p(V ) dV =
1
Ļ
arcsin
Vb
A
ā arcsin
Va
A
The diļ¬erence between the measured and expected probability of observing a speciļ¬c output code
is a function of that speciļ¬c code width, which can be used to calculate the diļ¬erential nonlinearity.
Due to the statistical nature of this test, the length of the data record for the histogram test
can be quite large [9]. The number of samples, M, required for a high-conļ¬dence measurement is
M =
Ļ2Nā1Z2
Ī±/2
Ī²2
where N is the number of bits, Ī² is the DNL-measurement resolution, and for a 99% conļ¬dence
level, ZĪ±/2 = 2.576. Thus for a 6-bit A/D converter, and a DNL-measurement resolution of 0.1 LSB
M =
Ļ2Nā1Z2
Ī±/2
Ī²2
=
Ļ25(2.576)2
(0.1)2
= 67, 000
If the sample size is large enough, this test will work with an asynchronous input signal, however
a coherent input signal with unique samples (odd and prime frequency ratio, as explained above)
is also possible. Sampling an input signal harmonically related to the sampling frequency would
create the same problems that occur with the FFT tests. Figure 13 shows the histogram for a 6-bit
A/D converter using an insuļ¬cient number of points, while Figure 14 shows a properly smooth
histogram result from using enough data.
Once the histogram data is collected, the experimental code widths are from the measured data
Hk using the expected probability [5]
Pk =
1
Ļ
arcsin
(k + 1)VQ
A
ā arcsin
kVQ
A
The diļ¬erential nonlinearity is thus
DNLk = 1 LSB
Hk
Pk
ā 1
However, this method is sensitive to errors in the measurement of the input sine wave amplitude A.
13
14. 0
0.01
0.02
0.03
0.04
0.05
0.06
0 10 20 30 40 50 60
probability
output code
Figure 13: Histogram of 1000 points for 6-bit A/D converter (insuļ¬cient).
0
0.01
0.02
0.03
0.04
0.05
0.06
0 10 20 30 40 50 60
probability
output code
Figure 14: Histogram of 100,000 points for 6-bit A/D converter.
14
15. A better method is to calculate the INL and DNL from a cumulative histogram [9]. First, the
oļ¬set is found by equating the number of positive samples Mp and the number of negative samples
Mn
Mn =
2Nā1
k=1
Hk Mp =
2N
k=2Nā1+1
Hk
VOS =
AĻ
2
sin
Mp ā Mn
Mp + Mn
Second, the transition voltages are found from
Vj = āA cos
ļ£«
ļ£
Ļ
M
j
k=0
Hk
ļ£¶
ļ£ø
Once the transition voltages are known, the INL and DNL follow
INLj =
Vj ā V1
1 LSB
DNLj =
Vj+1 ā Vj
1 LSB
This method makes the amplitude A a linear factor in the calculations, which reduces the sensitivity
to errors in A, and makes the ļ¬nal result easily normalizable.
15
16. 2.4 Sine Wave Curve Fit for ENOB
Another way to calculate the eļ¬ective number of bits is by using a sine wave curve ļ¬t [7]. A least-
squared-error sine wave is ļ¬t to the measured data, and compared to the input waveform. The
resulting rms error of the curve ļ¬t, E, is a measure of the eļ¬ective number of bits lost due to A/D
converter error sources. The eļ¬ective number of bits (ENOB) can be calculated from
ENOB = N ā log2
Erms
VQ/
ā
12
Since the frequency of the input and the output must be the same, only the amplitude, oļ¬set, and
phase of the output sinusoid must be found.
2.4.1 General Form
The ļ¬xed-frequency sine-wave curve-ļ¬t method [10] is used to ļ¬nd the best ļ¬t. The sinusoid to ļ¬t
to the output data is
xn = A cos(Ļtn) + B sin(Ļtn) + C
where tn are the sample times, Ļ is the input frequency (in radians/second) and A, B, and C are
the ļ¬t parameters. The squared error between the samples yn and the curve ļ¬t is
E =
M
k=1
(yk ā xk)2
=
M
k=1
[yk ā A cos(Ļtk) ā B sin(Ļtk) ā C]2
The error is minimized by setting the partial derivatives with respect to the ļ¬t parameters to zero
0 =
āE
āA
= ā2
M
k=1
[yk ā A cos(Ļtn) ā B sin(Ļtn) ā C] cos(Ļtk)
0 =
āE
āB
= ā2
M
k=1
[yk ā A cos(Ļtn) ā B sin(Ļtn) ā C] sin(Ļtk)
0 =
āE
āC
= ā2
M
k=1
[yk ā A cos(Ļtn) ā B sin(Ļtn) ā C]
Deļ¬ning Ī±k = cos(Ļtk) and Ī²k = sin(Ļtk) and rearranging terms gives a set of linear equations
M
k=1
ykĪ±k = A
M
k=1
Ī±2
k + B
M
k=1
Ī±kĪ²k + C
M
k=1
Ī±k
M
k=1
ykĪ²k = A
M
k=1
Ī±kĪ²k + B
M
k=1
Ī²2
k + C
M
k=1
Ī²k
M
k=1
yk = A
M
k=1
Ī±k + B
M
k=1
Ī²k + CM
These equations can be expressed as a single linear equation Y = UX
M
k=1
ļ£®
ļ£Æ
ļ£°
ykĪ±k
ykĪ²k
yk
ļ£¹
ļ£ŗ
ļ£» =
ļ£«
ļ£¬
ļ£
M
k=1
ļ£®
ļ£Æ
ļ£°
Ī±2
k Ī±kĪ²k Ī±k
Ī±kĪ²k Ī²2
k Ī²k
Ī±k Ī²k 1
ļ£¹
ļ£ŗ
ļ£»
ļ£¶
ļ£·
ļ£ø
ļ£®
ļ£Æ
ļ£°
A
B
C
ļ£¹
ļ£ŗ
ļ£»
16
17. This linear equation has a solution X = Uā1Y . The total squared error from above is
E =
M
k=1
[yk ā AĪ±k ā BĪ²k ā C]2
and can be rewritten as
E =
M
k=1
[y2
k ā 2AykĪ±k ā 2BykĪ²k ā 2Cyk + A2
Ī±2
k + 2ABĪ±kĪ²k + 2ACĪ±k + B2
Ī²2
k + 2BCĪ²k + C2
]
E =
M
k=1
y2
k ā 2XT
Y + XT
UX
The rms error of the curve ļ¬t is then
Erms =
E
M
So the eļ¬ective number of bits (ENOB) is
ENOB = N ā
1
2
log2
12E
V 2
QM
2.4.2 Simpliļ¬ed Coherent Form
One advantage of the curve-ļ¬t method of ļ¬nding the eļ¬ective number of bits is, unlike the methods
that use the FFT and SNDR, it does not require windowing or coherent sampling. However, if
coherent sampling is used, such that the expected data record has an integer number of sine-wave
cycles, the linear equation above simpliļ¬es signiļ¬cantly. The sums over Ī±k, Ī²k, and Ī±kĪ²k vanish,
and
M
k=1
Ī±2
k =
M
k=1
Ī²2
k =
M
2
The linear equation becomes
M
k=1
ļ£®
ļ£Æ
ļ£°
ykĪ±k
ykĪ²k
yk
ļ£¹
ļ£ŗ
ļ£» =
ļ£®
ļ£Æ
ļ£°
M/2 0 0
0 M/2 0
0 0 M
ļ£¹
ļ£ŗ
ļ£»
ļ£®
ļ£Æ
ļ£°
A
B
C
ļ£¹
ļ£ŗ
ļ£»
which has simple, closed-form solutions
A =
2
M
M
k=1
ykĪ±k B =
2
M
M
k=1
ykĪ²k C =
1
M
M
k=1
yk
The least squared error simpliļ¬es to
E =
M
k=1
y2
k ā 2AykĪ±k ā 2BykĪ²k ā 2Cyk + A2
Ī±2
k + 2ABĪ±kĪ²k + 2ACĪ±k + B2
Ī²2
k + 2BCĪ²k + C2
=
M
k=1
y2
k ā MA2
ā MB2
ā 2MC2
+
M
2
A2
+ 0 + 0 +
M
2
B2
+ 0 + MC2
=
M
k=1
y2
k ā
M
2
(A2
+ B2
+ 2C2
)
17
18. The eļ¬ective number of bits (ENOB) remains
ENOB = N ā
1
2
log2
12E
V 2
QM
2.4.3 Comparison of Methods
Clearly, calculating the eļ¬ective number of bits using an FFT and the signal-to-noise-and-distortion
ratio should produce the same result as the sine-wave curve-ļ¬t method. Figure 15 shows part of
a 4096-point quantized sine wave with added noise, plotted with the best-ļ¬t sine wave, calculated
as described above. The eļ¬ective number of bits from the curve ļ¬t is 3.02. Figure 16 shows the
FFT of the same data. The eļ¬ective number of bits calculated from SNDR is 3.01, which matches
nicely with the curve-ļ¬t result.
0
10
20
30
40
50
60
70
0 20 40 60 80 100
outputcode
sample index
Figure 15: Partial data plot showing sine wave and added noise, along with best-ļ¬t sine wave.
ENOB calculated from curve ļ¬t is 3.01.
18
19. 0.001
0.01
0.1
1
0 50 100 150 200 250 300 350 400 450 500
normalizedamplitude
FFT frequency bin
Figure 16: Partial FFT showing sine wave and added noise. ENOB calculated from SNDR is 3.02.
2.5 Overall Noise and Aperture Jitter
Overall noise of an A/D converter can be measured by grounding the input of the A/D converter
(in a unipolar A/D converter, the input should be connected to a mid-scale constant voltage source)
and accumulating a histogram. Only the center code bin should have counts in it. Any spread in
the histogram around the center code bin is caused by noise in the A/D converter. This test can
also be used to determine the oļ¬set of the A/D converter as in the histogram test,
Aperture jitter is measured by repeatedly sampling the same voltage of the input waveform.
For example, a sine wave input is used, and the A/D converter is triggered to repeatedly sample
the positive-slope zero crossing. If the input sine wave and the sampling clock are generated from
phase-locked sources, there should be no spread in the output digital codes from this measurement.
However, a real A/D converter will produce a spread in output codes due to aperture jitter.
The aperture jitter is calculated from a histogram of output codes produced from this measure-
ment. For an input sine wave sampling at the zero crossings, the aperture jitter is
ta =
Vrms
2ĻfA
where A is the amplitude and f is the frequency of the input sine wave. As the amplitude of the
input increases, the slope at the zero crossings increases, and the spread of output codes should
proportionally increase due to aperture jitter.
19
20. References
[1] Walter Kester. Measure ļ¬ash-ADC performance for trouble-free operation. EDN, pages 103ā
114, February 1, 1990.
[2] Walter Kester. Flash ADCs provide the basis for high-speed conversion. EDN, pages 101ā110,
January 4, 1990.
[3] Analog Devices Inc. AD678 12-bit 200 kSPS complete sampling ADC. Datasheet, 2000.
[4] Robert H. Walden. Analog-to-digital converter survey and analysis. IEEE Journal on Selected
Areas in Communication, 17(4):539ā550, April 1999.
[5] Brendan Coleman, Pat Meehan, John Reidy, and Pat Weeks. Coherent sampling helps when
specifying DSP A/D converters. EDN, pages 145ā152, October 15, 1987.
[6] Fredric J. Harris. On the use of windows for harmonic analysis with the discrete fourier
transform. Proceedings of the IEEE, 66(1):51ā83, January 1978.
[7] Walter Kester. DSP test techniques keep ļ¬ash ADCs in check. EDN, pages 133ā142, January
18, 1990.
[8] Walter Kester. Test video A/D converters under dynamic conditions. EDN, pages 103ā112,
August 18, 1982.
[9] Joey Doernberg, Hae-Seung Lee, and David A. Hodges. Full speed testing of A/D converters.
IEEE Journal of Solid State Circuits, 19(6):820ā827, December 1984.
[10] Brian J. Frohring, Bruce E. Peetz, Mark A. Unkrich, and Steven C. Bird. Waveform recorder
design for dynamic performace. Hewlett-Packard Journal, 39(1):39ā48, February 1988.
Copyright c 2002 Kent Lundberg. All rights reserved.
20