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  Keiji TATSUMI  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.