Least Mean Square Adaptive Digital Background Calibration of Pipelined Analog-to-Digital Converters

Abstract

We present an adaptive digital technique to calibrate pipelined analog-to-digital converters (ADCs). Rather than achieving linearity by adjustment of analog component values, the new approach infers component errors from conversion results and applies digital postprocessing to correct those results. The scheme proposed here draws close analogy to the channel equalization problem commonly encountered in digital communications. We show that, with the help of a slow but accurate ADC, the proposed code-domain adaptive finite-impulse-response filter is sufficient to remove the effect of component errors including capacitor mismatch, finite op-amp gain, op-amp offset, and sampling-switch-induced offset, provided they are not signal-dependent. The algorithm is all digital, fully adaptive, data-driven, and operates in the background. Strong tradeoffs between accuracy and speed of pipelined ADCs are greatly relaxed in this approach with the aid of digital correction techniques. Analog precision problems are translated into the complexity of digital signal-processing circuits, allowing this approach to benefit from CMOS device scaling in contrast to most conventional correction techniques.