Behavior-Level Analysis of a Successive Stochastic Approximation Analog-to-Digital Conversion System for Multi-Channel Biomedical Data Acquisition

Sadahiro TANI
Toshimasa MATSUOKA
Yusaku HIRAI
Toshifumi KURATA
Tomohiro ASANO
Masayuki UEDA
Takatsugu KAMATA

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A    No.10    pp.2073-2085
Publication Date: 2017/10/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E100.A.2073
Type of Manuscript: PAPER
Category: Analog Signal Processing
SAR-ADC,  DAC error calibration,  stochastic A/D conversion,  mismatch,  machine learning,  

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In the present paper, we propose a novel high-resolution analog-to-digital converter (ADC) for low-power biomedical analog front-ends, which we call the successive stochastic approximation ADC. The proposed ADC uses a stochastic flash ADC (SF-ADC) to realize a digitally controlled variable-threshold comparator in a successive-approximation-register ADC (SAR-ADC), which can correct errors originating from the internal digital-to-analog converter in the SAR-ADC. For the residual error after SAR-ADC operation, which can be smaller than thermal noise, the SF-ADC uses the statistical characteristics of noise to achieve high resolution. The SF-ADC output for the residual signal is combined with the SAR-ADC output to obtain high-precision output data using the supervised machine learning method.