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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 Publication 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 Keyword: SAR-ADC, DAC error calibration, stochastic A/D conversion, mismatch, machine learning, Full Text: PDF>>
Summary: 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. | |||||
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