Performance More Than Bits and Megahertz

In todayï¿½s battle for data fidelity, ENOB can provide a more complete picture of data acquisition system performance.

As A/D converters (ADCs) 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 because of the complex interactions between the ADC and the preceding signal conditioning circuits. 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 systems and their key components, the ADCs, are increasingï¿½and they donï¿½t 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 data acquisition systems and ADCs remains high.

One specification that ADC and data acquisition board vendors have begun touting is the SFDR, often quoting it as an indicator of the digital outputï¿½s 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 ratio of the strength of the fundamental signal to the strongest spurious signal in the output. In many cases, the spurious signal is the result of nonlinearity 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 data acquisition 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, this closer look begins by considering all of the elements in a data acquisition system. A data acquisition module contains several key functions including an anti-aliasing filter, a sample-and-hold circuit, and in many cases, an analog multiplexer to make one ADC handle multiple input signals (Figure 1). Nonlinearity in any of these elements can generate spurious signals that can affect the achievable SFDR.

Figure 1. Components Commonly Found in a Data Acquisition System

A list of the many sources of spurious signals includes the following:

ï¿½ Sample-Hold Nonlinearity.
ï¿½ ADC Nonlinearity.
ï¿½ Signal Multiplexing.
ï¿½ System Clock Noise.
ï¿½ DC/DC Converter Noise.
ï¿½ Adjacent Channel Noise.
ï¿½ Channel Overload.

Spurious signals also occur within the anti-aliasing filter as a result of the high signal bandwidth available in todayï¿½s ADCs. The purpose of an anti-aliasing filter is to limit the input signalï¿½s bandwidth to eliminate high-frequency components.

A rule of sampled data systems is that the input signalï¿½s 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 that band.

The reality is that filters are not perfect. As shown in Figure 2, the roll-off characteristics of a practical filter mean that it still will pass some of the signals above the filterï¿½s 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.

Figure 2. Spurious Signals From a Nonideal Anti-Alias Filter

The significance of SFDR in many systems is its impact 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. The practical ramification of this ambiguity is that the spurious signals can mask desired signals.

For example, in a motor maintenance application, the data acquisition system is looking for harmonics of the motor rotation rate in the motorï¿½s vibration spectrum. The presence of growing harmonics is an indication of motor wear. When the data acquisition system itself creates spurious signals, those harmonics may be masked until they become stronger, reducing the systemï¿½s capability to make an early prediction of motor failure.

In another example, the presence of spurious signals in digitized audio reduces a systemï¿½s capability to reproduce very soft passages. These signals manifest themselves as hiss in the audio signal, reducing the signal quality.

The test setup for measuring SFDR involves generating a pure sinusoidal input signal that is accurate to at least 0.001% and having an amplitude within 1 dB of the data acquisition systemï¿½s maximum input range. Then, a fast Fourier transform (FFT) is performed on the output.

The frequency spectrum that the FFT produces allows direct measurement of the SFDR: simply compare the magnitude of the strongest spurious signal with the magnitude of the input signal. 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 signal crosstalk from other inputs.

More Than SFDR
The same test setup is used to yield another metric: the effective number of bits (ENOB). ENOB is similar to SFDR because it compares the input signal strength with the strength of spurious signals. It differs because ENOB compares the rms value of the input signal with the mean value of the root-sum-squares of all other spectral components, not just the strongest spurious signal. The ENOB tells users how many of the systemï¿½s output bits will contain useful information because of the total noise level. SFDR only indicates the magnitude of the strongest spurious signal.

As a metric, the ENOB is a broader measure of a data acquisition systemï¿½s performance than SFDR. For the analog front end, for instance, ENOB 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 also contribute to ENOB. So do system electrical noise, distortions that the ADC introduces, and the effects of over-driving the filter op-amps when the input signal is over-range.

The ENOB metric includes the effects of SFDR but provides a more accurate overall picture of a data acquisition systemï¿½s potential performance. This does not mean that the SFDR specification has no value. Because it focuses specifically on spurious signals rather than random noise, the SFDR spec provides some guidance about where improvements can be made to the data acquisition 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 nonlinearity in the signal-conditioning chain as well as the ADC. If the ADCï¿½s specified linearity does not account for all of the harmonic energy, the front end should be checked.

The spurious signals introduced by folding the anti-aliasing filterï¿½s 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:

Use a filter with a sharper roll-off, which generally implies a more complex filter.
Increase the sample clock frequency so the folding involves signal components farther out in the filterï¿½s roll-off curve.

When the spurious signals appear tied to the channel switching frequency of a multiple-input ADC, designers can look for noise in the multiplexer. They also can 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, eliminates the use of a multiplexer and uses one ADC for each channel (Figure 3). This allows a simultaneous sample-and-hold for each input signal, eliminating crosstalk and switching noise, and ensures that those signals can use 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.

Figure 3. Six Independent, Successive-Approximation A/D Converters With Track-and-Hold Circuitry

Conclusion
The SFDR specification has value for the data acquisition 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 data acquisition systemï¿½s 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 use the SFDR specification, designers need to consider how SFDR has been measured and whether it applies to an entire data acquisition module or just the ADC. If it is just the ADC, they should realize that the analog front-end design may degrade the achievable data acquisition performance below the values indicated by SFDR. The SFDR specification then can serve as a pointer to problems within the front end and help designers maximize the fidelity of their data acquisition system.

About the Author
Tim Ludy is a product marketing manager at Data Translation. He graduated from Northeastern University with a degree in computer science. Data Translation, 100 Locke Dr., Marlboro, MA 01752, 800-525-8528, e-mail: [email protected]

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