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Article Title:
The Battle for Data Fidelity: Understanding the SFDR Spec

Article Description:
As A/D converters (ADC) and data acquisition boards increase 
their bandwidth, more and more are including the spurious free 
dynamic range (SFDR) specification as an indicator of their 
fidelity. The converter is not the only source of spurious 
signals; however, because of complex interactions between the ADC 
and the signal conditioning circuits that invariably precede it.

Additional Article Information:
1530 Words; formatted to 65 Characters per Line
Distribution Date and Time: Wed Mar 22 01:39:05 EST 2006

Written By:     Tim Ludy
Copyright:      2006
Contact Email:  mailto:[EMAIL PROTECTED]

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The Battle for Data Fidelity: Understanding the SFDR Spec
Copyright © 2006 Tim Ludy
Data Translation

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As A/D converters (ADC) and data acquisition boards increase 
their bandwidth, more and more are including the spurious free 
dynamic range (SFDR) specification as an indicator of their 
fidelity. The converter is not the only source of spurious 
signals; however, because of complex interactions between the ADC 
and the signal conditioning circuits that invariably precede it. 
The key to properly interpreting this specification lies in 
understanding the sources of spurious signals and how SFDR is 
measured. Like everything else in the world of electronics, the 
speed and bandwidth of data acquisition (DAQ) systems and their 
key components, the ADCs, are increasing. And they don't show any 
signs of stopping. The need for speed in applications such as 
high-speed data acquisition is continually pushing the limits of 
ADCs. At the same time, the need for precision and accuracy in 
the DAQs and ADCs remains high. One of the specifications that 
ADC and DAQ board vendors have begun touting is the spurious free 
dynamic range (SFDR). They often quote the SFDR specification as 
an indicator of the digital output's fidelity to the original 
signal. While there is an element of truth in that implication, 
the SFDR specification can be misleading if not properly 
interpreted. The basic definition of the SFDR specification is 
simple. It is the strength ratio of the fundamental signal to the 
strongest spurious signal in the output. In many cases, the 
spurious signal is the result of non-linearity in the A/D 
conversion, hence the interpretation of SFDR as an indicator of 
fidelity. But a number of other sources of strong spurious 
signals may be present in the DAQ system, so the SFDR 
specification requires a closer look.

Because ADCs are never used as the only element between the input 
signal and the digital output, the place to begin this closer 
look is by considering all of the elements in a DAQ system. As 
shown in Figure 1, a DAQ module contains several key functions, 
including a signal-conditioning filter, a sample-and-hold 
circuit, and in many cases an analog multiplexer to make one ADC 
handle multiple input signals. Non-linearity in any of these 
elements can generate spurious signals that can affect the 
achievable SFDR.

Another source of spurious signals occurs within the anti-
aliasing filter as a result of the high signal bandwidth 
available in today's ADCs. The purpose of an anti-aliasing filter 
is to limit the input signal's bandwidth to eliminate high-
frequency components. A rule of sampled data systems is that the 
input signal's spectrum gets folded around a frequency one-half 
that of the sample clock. An ideal anti-aliasing filter would 
pass all signals in the band of interest and block all signals 
outside of that band.

The reality is, however, that filters are not perfect. As shown 
in Figure 2, the roll-off characteristics of a practical filter 
mean that it will still pass some of the signals above the 
filter's cutoff frequency. Depending on where that cutoff occurs 
relative to the sampling frequency, the folded signal spectrum 
may overlap the input signal spectrum. If the filtered signal 
contains any energy in this overlap band, that energy appears as 
spurious signals in the output, affecting the SFDR.

As a result of these numerous spurious sources, the significance 
of SFDR in many systems is not the converted signal's fidelity, 
but the impact of the spurious signal as a noise source. In 
effect, SFDR indicates the lowest-energy input signal that can be 
distinguished from spurious signals. Any signal below the SFDR 
cannot be reliably identified as a true signal instead of as a 
spurious one. The practical ramification of this ambiguity is 
that the spurious signals can mask desired signals.

In a motor maintenance application, for example, the DAQ is 
looking for harmonics of the motor rotation rate in the motor's 
vibration spectrum. The presence of growing harmonics is an 
indication of motor wear and the need for replacement. When the 
DAQ itself creates spurious signals, those harmonics may be 
masked until they become stronger, reducing the system's ability 
to make an early prediction of motor failure. In another example, 
the presence of spurious signals in digitized audio reduces a 
system's effectiveness. In the audio case, these signals manifest 
as “hiss” in the audio signal, reducing the signal quality.

The anti-aliasing filter is only one example of spurious signal 
sources. A more complete list includes:

 * Sample-hold non-linearity 
 * ADC non-linearity 
 * Signal multiplexing 
 * System clock noise 
 * DC/DC converter noise 
 * Adjacent channel noise 
 * Channel overload (driving op-amp to rails)

Because of the many sources of spurious signals, SFDR of the ADC 
alone is not a sufficient measurement of the achievable signal 
dynamic range. The measurement must be made in the context of a 
full DAQ system.

The test setup for measuring SFDR, shown in Figure 3, involves 
generating a pure sinusoidal input signal (accurate to at least 
0.001%) with strength within 1 dB of the DAQ system's maximum 
input range. Then, perform an FFT (Fast Fourier Transform) on the 
output. The frequency spectrum that the FFT produces allows 
direct measurement of the SFDR. In addition, performing the FFT 
on the output of an adjacent channel, which has its input 
grounded, provides a measure of the spurious signals coming from 
the rest of the system as well as coupling of signals from other 

Comparing the two measurements will also provide a metric known 
as the effective number of bits (ENOB). The ENOB tells users how 
many of the system's output bits will contain useful information, 
typically a value lower than the resolution of the ADC. It is a 
particularly useful metric, as it measures the performance of the 
entire DAQ system, not just the ADC, and it does so under 
dynamic, real-world conditions.

As a metric, the ENOB is a more useful measure of a DAQ system's 
performance. For the analog front end, for instance, it will 
detect such things as interactions between the over-voltage 
protection circuits and EMI filters. Noise from gain-setting 
resistors within the instrumentation amplifier, amplifier and 
sample-and-hold bandwidth errors, and the effects of acquisition 
time, channel-to-channel offset, and channel crosstalk in the 
input multiplexer all contribute to ENOB. So do system electrical 
noise, distortions that the ADC introduces (a component of SFDR) 
and the effects of over-driving the filter opamps when the input 
signal is over-range.

The ENOB metric includes the effects of SFDR, but provides a more 
accurate overall picture of a DAQ system's potential performance. 
This does not mean that the SFDR specification has no value, 
however. Because it focuses specifically on spurious signals 
rather than random noise, it provides some guidance as to where 
improvements can be made to the DAQ system.

When spurious signals are large relative to random noise, the 
frequency of the spurious signal helps identify the source. Pure 
harmonics of the input frequency, for instance, can be due to 
non-linearity in the signal conditioning chain as well as the 
ADC. If the ADC's specified linearity does not account for all of 
the harmonic energy, the front-end should be checked.

The spurious signals introduced by folding of the anti-aliasing 
filter's output around the frequency at half the sample clock 
rate can be identified by their concentration at the high end of 
the filter bandwidth. When those signals set the SFDR limit, they 
can be reduced in two ways. One is to use a filter with a sharper 
roll-off, which generally implies a more complex filter. The 
other is to increase the sample clock frequency, so that the 
folding involves signal components further out in the filter's 
roll-off curve.

When the spurious appear tied to the channel switching frequency 
of a multiple-input ADC, designers can look for noise in the 
multiplexer. They can also look at the slew rate and settling 
time of the sample-hold circuit, making sure that converted 
signals are not being affected by the values on adjacent 
channels. An alternative, increasingly available due to 
semiconductor process improvements, is to eliminate the use of a 
multiplexer and use one ADC for each channel. This allows a 
simultaneous sample-and-hold for each input signal, eliminating 
cross-talk and switching noise, and ensures that those signals 
can utilize the full sample rate of the ADCs rather than just a 
fraction. This increase also helps move the folding point for the 
anti-aliasing filter.

Thus, the SFDR specification has value for the DAQ system 
designer and user, but as a metric of data fidelity it has 
limits. In its place, the ENOB metric provides a more complete 
picture of a DAQ system's performance. The ENOB value 
incorporates the effects that SFDR aims to measure, as well as 
virtually every other noise source and distortion in the system.

To properly utilize the SFDR specification, designers need to 
consider how SFDR has been measured, and whether it applies to an 
entire DAQ module or just the ADC. If it is just the ADC, they 
should realize that the analog front-end design may degrade the 
achievable DAQ performance below the values indicated by SFDR. 
The SFDR specification can then serve as a pointer to problems 
within the front-end, and help designers maximize the fidelity of 
their DAQ system. 

Tim Ludy is a Product Marketing Manager with <a href="";>Data 
Mr. Ludy graduated from Northeastern University with a degree 
in Computer Science. - email: [EMAIL PROTECTED]



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