Using sine wave histograms to estimate analog-to-digital converter dynamic error functions

Abstract

This paper describes a new method for developing analog-to-digital converter (ADC) error function models using modified sinewave histogram methods. The error models may be used to digitally compensate for nonlinearities introduced by the converter. The histogram modification involves sorting of converter output samples based upon an estimated associated input derivative signal. This error model is based upon a previously unpublished result which shows that sinewave histograms yield distinctly different expected errors for each state based upon input signal slope associated with each output sample. This result thus provides a dynamic dependence for expected errors measured by means of histogram methods. Sorted sinewave histograms are used to estimate slope dependent expected errors at each ADC output state (code). The method provides improved error representation by providing error basis functions for every output code. Simulated results prove that this method removes all slope dependent errors for complex ADC architectures while experimental results for an 8-bit 200 MSPS ADC yielded more than 10 dB improvement in spurious-free-dynamic-range (SFDR) over the full Nyquist band. The new method is thus shown to possess wideband dynamic error character.