An Integrated CMOS/Memristor Bio-Processor for Re-Configurable Neural Signal Processing

Grahame Reynolds, Xiongfei Jiang, Alex Serb, Themis Prodromakis, Shiwei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper proposes a bio-processor for neural signal analysis. The device architecture features an analogue Front-End and a Process Element, the latter can be scaled as an array. Rather than a single dedicated algorithm, the Process Element supports multiple analysis modes, utilising the analogue behaviour of memristors. When used as part of an array struc-ture, each Process Element can be programmed independently and furthermore, the array elements can be electrically inter-connected in an arbitrary manner. The device facilitates an inter-network of in-memory computation units, i.e. an inter-network of functions. This supports construction of a system that is highly scalable, re-configurable and thus adaptive. The device enables multi-functional neural recording and processing, for early stage signal exploration. The device has been implemented using a standard 180nm CMOS process with the addition of back-end-of-line (BEOL) memristor deposition. Although targeted at neural signal analysis, the device and the architecture described is considered general purpose and may find application within other disciplines.
Original languageEnglish
Title of host publication2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Publication statusE-pub ahead of print - 18 Jan 2024
EventArtificial Intelligence BioMedical Circuits And Systems For Health - Westin Harbour Castle Hotel, Toronto, Canada
Duration: 19 Oct 202321 Oct 2023

Publication series

NameIEEE Biomedical Circuits and Systems (BIOCAS)
ISSN (Print)2163-4025
ISSN (Electronic)2766-4465


ConferenceArtificial Intelligence BioMedical Circuits And Systems For Health
Abbreviated titleBIOCAS 2023
Internet address


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