SECTION 5 UNDERSAMPLING APPLICATIONS Fundamentals of Undersampling Increasing ADC SFDR and ENOB using External SHAs Use of Dither Signals to Increase ADC Dynamic Range Effect of ADC Linearity and Resolution on SFDR and Noise in Digital Spectral Analysis Applications Future Trends in Undersampling ADCs 1 SECTION 5 UNDERSAMPLING APPLICATIONS Walt Kester An exciting new application for wideband, low distortion ADCs is called undersampling, harmonic sampling, bandpass sampling, or Super-Nyquist Sampling. To understand these applications, it is necessary to review the basics of the sampling process. The concept of discrete time and amplitude sampling of an analog signal is shown in Figure . The continuous analog data must be sampled at discrete intervals, ts, which must be carefully chosen to insure an accurate representation of the original analog signal. It is clear that the more samples taken (faster sampling rates), the more accurate the digital representation, but if fewer samples are taken (lower sampling rates), a point is reached where critical information about the signal is actually lost. This leads us to the statement of Shannon's Information Theorem and Nyquist's Criteria given in Figure . Most textbooks state the Nyquist theorem along the following lines: A signal must be sampled at a rate greater than twice its maximum frequency in order to ensure unambiguous data. The general assumption is that the signal has ponents from dc to some upper value, fa. The Nyquist Criteria thus requires sampling at a rate fs > 2fa in order to avoid overlapping ponents. For signals which do not extend to dc, however, the minimum required sampling rate is a function of the bandwidth of the signal as well as its position in the frequency spectrum. SAMPLING AN ANALOG SIGNAL Figure 2 SHANNON’S INFORMATION THEOREM AND NYQUIST’S CRITERIA Shannon:
An Analog Signal with a Bandwidth of fa Must be Sampled at a Rate of fs>2
