Analog-to-digital conversion and quantization constitute the topic of this thesis. Post-correction of analog-to-digital converters (ADCs) is considered in particular. ADCs usually exhibit non-ideal behavior in practice. These non-idealities spawn distortions in the converters output. Whenever the errors are systematic, it is possible to mitigate them by mapping the output into a corrected value. The work herein is focused on problems associated with post-correction using look-up tables. All results presented are supported by experiments or simulations.
The first problem considered is characterization of the ADC. This is in fact an estimation problem, where the transfer function of the converter should be determined. This thesis deals with estimation of quantization region midpoints, aided by a reference signal. A novel estimator based on order statistics is proposed, and is shown to have superior performance compared with the sample mean traditionally used.
The second major area deals with predicting the performance of an ADC after post-correction. A converter with static differential nonlinearities and random input noise is considered. A post-correction is applied, but with limited (fixed-point) resolution in the corrected values. An expression for the signal-to-noise and distortion ratio after post-correction is provided. It is shown that the performance is dependent on the variance of the differential nonlinearity, the variance of the random noise, the resolution of the converter and the precision of the correction values.
Finally, the problem of addressing, or indexing, the correction look-up table is dealt with. The indexing method determines both the memory requirements of the table and the ability to describe and correct dynamically dependent error effects. The work here is devoted to state-space--type indexing schemes, which determine the index from a number of consecutive samples. There is a tradeoff between table size and dynamics: more samples used for indexing gives a higher dependence on dynamic, but also a larger table. An indexing scheme that uses only a subset of the bits in each sample is proposed. It is shown how the selection of bits can be optimized, and the exemplary results show that a substantial reduction in memory size is possible with only marginal reduction of performance.