Equalization of Distortion in A/D Converters

Abstract: Modern communication systems require A/D converters with very high sample rate and high accuracy. CMOS technology is suitable for integrating A/D converters on a chip at a low cost and low power consumption. However, the CMOS manufacturing process is quite inaccurate, which leads to errors in the A/D converters.Traditionally the A/D converters are calibrated after they are manufactured to correct these errors. This is a time-consuming and costly process. The characteristics of an A/D converter normally change during its lifetime due to, for instance, temperature changes and aging. This cannot be compensated for in the calibration.In this thesis, we investigate how errors in A/D converters can be estimated and corrected without the need for calibration. The estimation should be done using only the signal that is used in the application.Three different types of errors are discussed in this thesis. The first type of error is static nonlinear errors caused by component inaccuracies in CMOS technology. Two methods are proposed for estimation and correction of these errors. The most general method requires only that the amplitude distribution is smooth. With the other method the performance is a little better but it requires knowledge of the amplitude distribution of the input signal. The estimation methods are evaluated on simulated data and data from a real A/D converter.The second type of error is dynamic nonlinear errors in the sample-and hold circuit which inevitably occurs when the sample rate increases. Some ideas about how to correct these errors are discussed.The third type of error is timing errors in time interleaved A/D converters, where the idea is to increase sample rate by parallelization of the conversion. A method for estimation and correction of these errors is proposed. This method requires that most of the signal energy is limited to a frequency band below about 1=3 of the Nyquist frequency, but requires no other knowledge of the signal. This estimation method is evaluated on simulated data.