{"title":"Near-Sensor 2D Pattern Classification System Based on 3D Memristor Array","authors":"Raqibul Hasan, Tarek M. Taha, Md Shahanur Alam","doi":"10.1049/ell2.70192","DOIUrl":null,"url":null,"abstract":"<p>The proposed work demonstrates a 3D memristor crossbar-based pattern classification system. Analogue signals from a 2D sensor array are directly applied to the memristor crossbar circuit using TSVs for computation. The proposed near-sensor computing system reduces the wiring complexity between the sensor unit and the compute unit significantly. The 3D memristor crossbar occupies a very small area compared to an equivalent 2D crossbar. We have leveraged ex-situ training for the memristor-based neural network using the two extreme resistance levels of the device (<i>R</i><sub>ON</sub><i>, R</i><sub>OFF</sub>) which enables simple training circuit. To process a 100 × 100 sensor array, the proposed approach requires 5.6× less wiring between the sensor array and the memristor crossbar circuit compared to a non-stacked approach.</p>","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70192","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics Letters","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ell2.70192","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The proposed work demonstrates a 3D memristor crossbar-based pattern classification system. Analogue signals from a 2D sensor array are directly applied to the memristor crossbar circuit using TSVs for computation. The proposed near-sensor computing system reduces the wiring complexity between the sensor unit and the compute unit significantly. The 3D memristor crossbar occupies a very small area compared to an equivalent 2D crossbar. We have leveraged ex-situ training for the memristor-based neural network using the two extreme resistance levels of the device (RON, ROFF) which enables simple training circuit. To process a 100 × 100 sensor array, the proposed approach requires 5.6× less wiring between the sensor array and the memristor crossbar circuit compared to a non-stacked approach.
期刊介绍：
Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews.
Scope
As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below.
Antennas and Propagation
Biomedical and Bioinspired Technologies, Signal Processing and Applications
Control Engineering
Electromagnetism: Theory, Materials and Devices
Electronic Circuits and Systems
Image, Video and Vision Processing and Applications
Information, Computing and Communications
Instrumentation and Measurement
Microwave Technology
Optical Communications
Photonics and Opto-Electronics
Power Electronics, Energy and Sustainability
Radar, Sonar and Navigation
Semiconductor Technology
Signal Processing
MIMO
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