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CS5016 датащи(PDF) 25 Page - Cirrus Logic |
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CS5016 датащи(HTML) 25 Page - Cirrus Logic |
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25 / 40 page ![]() Since bits (and their errors) switch in and out throughout the transfer curve, their effect is sig- nal dependent. That is, harmonic and intermodulation distortion, as well as noise, can vary with different input conditions. Designing a system around characterization data is risky since transfer curves can differ drastically unit-to-unit and lot-to-lot. The CS5012A/14/16 achieves repeatable signal- to-noise and harmonic distortion performance using an on-chip self-calibration scheme. The CS5012A calibrates its bit weight errors to a small fraction of an LSB at 12-bits yielding peak distortion below the noise floor (see Figure 18). The CS5014 calibrates its bit weights to within ±1/16 LSB at 14-bits (±0.0004% FS) yielding peak distortion as low as -105 dB (see Fig- ure 21). The CS5016 calibrates its bit weights to within ±1/4 LSB at 16-bits (±0.0004% FS) yield- ing peak distortion as low as -105 dB (see Figure 23). Unlike traditional ADC’s, the linear- ity of the CS5012A/14/16 are not limited by bit-weight errors; their performance is therefore extremely repeatable and independent of input signal conditions. Quantization Noise The error due to quantization of the analog input ultimately dictates the accuracy of any A/D con- verter. The continuous analog input must be represented by one of a finite number of digital codes, so the best accuracy to which an analog input can be known from its digital code is ±1/2 LSB. Under circumstances commonly en- countered in signal processing applications, this quantization error can be treated as a random variable. The magnitude of the error is limited to ±1/2 LSB, but any value within this range has equal probability of occurrence. Such a prob- ability distribution leads to an error "signal" with an rms value of 1 LSB/ √12. Using an rms signal value of FS/ √8 (amplitude = FS/2), this relates to ideal 12-, 14-, and 16-bit signal-to-noise ratios of 74, 86, and 98 dB respectively. Equally important is the spectral content of this error signal. It can be shown to be approximately white, with its energy spread uniformly over the band from dc to one-half the sampling rate. Ad- vantage of this characteristic can be made by judicious use of filtering. If the signal is ban- dlimited, much of the quantization error can be filtered out, and improved system performance can be attained. FFT Tests and Windowing In the factory, the CS5012A/14/16 are tested us- ing Fast Fourier Transform (FFT) techniques to analyze the converter’s dynamic performance. A pure sinewave is applied to the CS5012A/14/16, and a "time record" of 1024 samples is captured and processed. The FFT algorithm analyzes the spectral content of the digital waveform and dis- tributes its energy among 512 "frequency bins." Assuming an ideal sinewave, distribution of en- ergy in bins outside of the fundamental and dc can only be due to quantization effects and errors in the CS5012A/14/16. If sampling is not synchronized to the input sine- wave, it is highly unlikely that the time record will contain an integer number of periods of the input signal. However, the FFT assumes that the signal is periodic, and will calculate the spectrum of a signal that appears to have large discontinui- ties, thereby yielding a severely distorted spectrum. To avoid this problem, the time record is multiplied by a window function prior to per- forming the FFT. The window function smoothly forces the endpoints of the time record to zero, thereby removing the discontinuities. The effect of the window in the frequency-domain is to con- volute the spectrum of the window with that of the actual input. Figure 17 shows an FFT computed from an ideal 12-bit sinewave. The quality of the window used for harmonic analysis is typically judged by its highest side-lobe level. The Blackman-Harris window used for testing the CS5014 and CS5016 has a maximum side-lobe level of -92 dB. Fig- CS5012A, CS5014, CS5016 DS14F8 2-31 CS5012A CS5014 CS5016 DS14F9 25 |
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