Incremental Learning for a Calibration of a High-precision SAR-ADC by Using the Bayesian Linear Regression

Abstract

In this paper, we discuss a high-precision and low-power analog-to-digital converter (ADC) which is required for wearable biomedical measurement sensors driven by a battery. In particular, we focus on the successive approximation register ADC (SAR-ADC), and propose its calibration algorithm using the machine learning. We derive a calibration function for the outputs of the SAR-ADC by taking into account its characteristics, and show the least squares method of determining the parameter values of the function to minimize the residual errors. Furthermore, from the practical viewpoint, we propose an incremental learning for the calibration, where additional data sets are selected on the basis of the Bayesian predictive distributions which are obtained at each additional learning step. Through numerical experiments, we observed that the mean residual errors obtained by the proposed method are less than 1 LSB, and the method needs a small amount of training data.