2016 Volume 29 Issue 3 Pages 136-142
Recently, a high-precision and low-power analog-to-digital converter (ADC) is required for a wearable biomedical measurement sensor which is driven by a battery. For achieving the purpose,a software-level calibration method was proposed for the successive approximation register ADC (SAR-ADC), which trains a calibration function with an incremental learning, and selects additional training data by using the Bayesian linear regression. Some numerical experiments showed that the desirable precision is obtained and the method needs a small amount of training data. However, since the additional training data specified by the selection method cannot necessarily be obtained in the practical viewpoint, its straightforward implementation is extremely difficult. Therefore, we propose a selection method of additional training data which can be obtained by using an inverse calibration,and moreover, we show the effectiveness of the proposed method through numerical experiments.