Self-calibrating A/D converter using T-model neural network

Zheng Tang*, Yuichi Shirata, Okihiko Ishizuka, Koichi Tanno

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

A self-calibrating analog-to-digital (A/D) converter employing a T-Model neural network is described. Errors of the converter due to offset and device mismatch are corrected by a simple error back propagation algorithm in the T-Model neural network. An experimental A/D converter using standard 5-μm CMOS IC circuits demonstrates high-performance analog-to-digital conversion and self-calibration.

Original languageEnglish
Pages (from-to)533-536
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume1
StatePublished - 1995
EventProceedings of the 1995 IEEE International Symposium on Circuits and Systems-ISCAS 95. Part 3 (of 3) - Seattle, WA, USA
Duration: 1995/04/301995/05/03

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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