A statistics-based digital background calibration technique for pipelined ADCs

Abstract

This paper introduces a digital background calibration technique for pipelined analog-to-digital converters (ADCs). The proposed method continuously measures and digitally corrects conversion errors resulting from residue amplifier gain error and nonlinearity. It is based on modulation of the residue voltage using a pseudorandom-noise sequence (PN). A least-mean-squares (LMS) algorithm is utilized to correct conversion errors arising from the residue amplifier non-idealities. Besides, a new statistics-based digitized residue distance estimation (DRDE) algorithm is proposed that allows the LMS algorithm to operate in the background without interrupting the normal operation of the ADC. The DRDE method extracts the residue amplifier non-idealities by evaluating the digitized residue voltage probability density function (PDF). Behavioral simulation results verify the usefulness of the proposed calibration technique and show that the signal-to-noise-and-distortion-ratio (SNDR) is improved from 43 to 71.9dB, in a 12-bit pipelined ADC.