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An95091 Application Note Understanding Effective Bits

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AN95091 APPLICATION NOTE UNDERSTANDING EFFECTIVE BITS Tony Girard, Signatec, Design and Applications Engineer INTRODUCTION One criteria often used to evaluate an Analog to Digital Converter (ADC) or data acquisition system is the effective number of bits achieved. The effective number of bits provides a means to evaluate the overall performance of a system. However, like any other parameter, an understanding of the theory behind the effective bits, and different methods used to determine effective bits is necessary to properly compare components and systems. The intent of this application note is to provide the basic theory behind effective bits, describe different methods used to determine effective bits, and to explain the limitations in its usage. NUMBER OF BITS VERSES EFFECTIVE BITS The number of bits in a data acquisition system is normally specified as the number of bits of the digitizer. Any data acquisition system, or ADC, has inherent performance limitations. When evaluating an acquisition system the effective number of bits provided by the system can useful in determining if the system is right for the application. There are many sources of error in an acquisition system. Considering a system in terms of effective bits, all error sources are included. Evaluation of system performance is made without the need to consider the individual error sources, all of which may not be characterized by the manufacturer. THE PERFECT SYSTEM A perfect data acquisition system is one in which the captured analog signal is free from noise and distortion. The system is free from any frequency dependent performance characteristics, up to the maximum sampling rate and the bandwidth limit of the system. In this system the only limitation to system performance would be the quantization error inherent in all sampling systems. Quantization error occurs because an analog signal is sampled and its level represented by a finite number. The output of an ADC has 2n levels, where n is the number of converter bits. For example, an 8 bit ADC has 28, or 256 levels. The bit which represents the smallest voltage change is called the least significant bit (lsb). Each sample has a quantization error of up to ±0.5 of the lsb due to the difference between the true analog voltage level and the level represented by the ADC output. 1 The quantization error defines a base line noise level which limits the systems ability to resolve small signals in a perfect system. This is commonly referred to as Signal to Noise ratio (S/N) which is the ratio of the Root Mean Square (RMS) of the largest signal divided by the RMS value of the noise. The maximum signal level is 2n bits and the noise is 1 bit. The higher the resolution of a system, the larger the maximum signal level with respect to the 1 bit quantization noise. The result is that higher resolution systems have a lower base line noise level resulting in a greater dynamic range. S The SNR of an ideal system is found using the maximum RMS signal level N ideal (VRMS ) and the quantization error ( QE RMS ). The “mean square” of the quantization error is found by integrating over the quantization voltage error range as follows: + Vq /2 2 V 2 ==E q QE MS ∫ dE − V 12 Vq /2 q The RMS value of the quantization error is found by taking the square root of the mean square error: VV2 QE ==qq RMS 12 12 where Vq is the quanitization voltage and is equal to 1 bit. The ideal signal to noise ratio can then be found as follows: 2 n 1 V = QE = RMS 22 RMS 12 2 n S V 212nn23 ====≅∗RMS 22 n 2 1225. N ideal QE RMS 1 22 2 12 S In terms of decibels: ≅∗20log ( 2n 1255 . )dB 10 N ideal S ≅+ or (.602ndB 176 . ) N ideal where n is the number of bits. 2 THE REAL SYSTEM No system is ideal. Every converter has non-linearity, noise from reference voltages, and aperture jitter. Every system adds noise to the analog signal being sampled, creates signal distortion (harmonics), and has some amount of clock jitter. All these elements, and more, contribute to system performance degradation with respect to the ideal system. In effect, all error sources combine to limit the effective resolution of a system. Because different error sources are dependent upon various operating conditions, the effective number of bits is also dependent upon the operating conditions. The amplitude of the test signal can change the level of distortion. Signal amplitudes near full scale should normally be used. Typically the frequency of the sampling clock has little affect on the effective number of bits. The generally accepted method is to use the systems maximum sampling frequency for effective bits characterization. The frequency and amplitude of the input analog signal have the largest affect on the effective number of bits. This is generally due both to distortion of the signal by the analog front end and the system noise increase due to clock jitter as the signal frequency is increased. In state-of-the-art systems in terms of sampling rate and analog bandwidth, the effective number of bits generally decreases significantly as the signal frequency increases. SYSTEM PERFORMANCE MEASUREMENTS The effective number of bits is determined by sampling a spectrally pure sinusoidal wave and determining the RMS signal and “noise” levels recorded by the system. In this case “noise” refers to anything which is not signal, this includes quantization noise, noise from the input signal, and any distortion of the signal. Often data acquisition system manufacturers specify a performance parameter called SINAD. SINAD is the ratio of the fundamental SINusoidal signal power acquired to the total Noise And Distortion. Because this parameter contains noise and distortion, it can be used directly to calculate effective bits. When Signal to Noise Ratio (SNR) is specified, it is often the ratio of the fundamental sinusoidal signal power to the noise, not including the harmonics. Figure 1 shows the relationship of SINAD to SNR in a typical application. Using SNR to calculate the effective bits of a system is acceptable in some applications. For example a communications system where the bandwidth of interest is small and it is known that no harmonics, including those above the Nyquist limit which will be aliased, will fall within that band. 3 SINAD VS SNR 44 42 40 38 36 dB 34 32 30 28 1 10 100 1000 Signal Frequency (MHz) SINAD SNR FIGURE 1 DATA ANALYSIS There are two data analysis methods used to determine SINAD, and therefore effective bits. One method uses time domain data analysis and the other uses frequency domain data analysis. Time domain analysis uses a complex algorithm to fit a perfect sine curve to the acquired time domain data points. The algorithm must match the sine-wave frequency, phase, amplitude, and offset to the data. The perfect wave is then subtracted from the data and the remaining data is noise and distortion. This is the method commonly used by some ADC manufacturers. Frequency domain analysis uses an FFT to convert time domain data to the frequency domain. The appropriate FFT frequency bins are then selected to determine SINAD. Signatec, and some ADC manufacturers, use the frequency domain analysis method. Signatec uses a 4096 point FFT with a Blackman-Harris window to convert the data to the frequency domain. The energy from a signal spreads over a number of bins in the FFT. This makes selection of bins for signal calculations important when using the frequency domain method. An improper bin selection will affect the SINAD/effective bits calculation. Using a number of frequency bins which is greater than the number which actually contain signal energy will result in summing noise energy into the signal, resulting in an erroneously high signal level. Using too few frequency bins to represent the fundamental will result in a fundamental power which is too low and a “noise” level which is too high. The first 4 bins of the FFT represent the dc term caused by offset shift and the FFT windowing function, and are excluded from analysis. The 9 bins centered 4 around the peak frequency are summed to determine the fundamental signal power and the remaining bins are considered “noise” for the SINAD calculation. Figures 2, 4, and 6 show the frequency domain plots of data acquired with Signatec’s DA500A, 500MHz digitizing rate, 500MHz analog bandwidth data acquisition system. Figures 3, 5, and 7 are exploded views of showing the frequency bins at the fundamental frequency. The shaded bins are the ones used to calculate the level of the fundamental frequency. FFT RESULT (25MHz) FFT BINS (25MHz) -10 -20 -30 -40 -50 -60 Amplitude (dB) -70 -80 24.0 24.3 24.5 24.8 25.0 25.3 25.5 25.8 26.0 0 50 100 150 200 250 Frequency (MHz) Frequency (MHz) FIGURE 2 FIGURE 3 FFT RESULT (95MHz) FFT BINS (95MHz) -10 -20 -30 -40 -50 -60 Amplitude (dB) -70 -80 93.0 93.4 93.7 94.1 94.5 94.8 95.2 95.6 95.9 0 50 100 150 200 250 Frequency (MHz) Frequency (MHz) FIGURE 4 FIGURE 5 FFT RESULT (230MHz) FFT BINS (230MHz) -10 -20 -30 -40 -50 -60 Amplitude (dB) -70 -80 228.0 228.5 229.0 229.5 230.0 230.5 231.0 0 50 100 150 200 250 Frequency (MHz) Frequency (MHz) FIGURE 6 FIGURE 7 5 The selection of the frequency used for SINAD and effective bits test is important. The signal frequency must be selected such that the harmonic terms are sufficiently separated from the fundamental so as not to be considered part of the signal.
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