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Complementary memtransistors for neuromorphic computing: How, what and whyOACSTPCDEI

中文摘要

Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing.On the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit technology.Therefore,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges discussed.Then the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,etc.Through exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.

Qi Chen;Yue Zhou;Weiwei Xiong;Zirui Chen;Yasai Wang;Xiangshui Miao;Yuhui He;

School of Integrated Circuits,Huazhong University of Science and Technology,Wuhan 430074,ChinaDepartment of Electrical and Computer Engineering,University of California,San Diego,USADipartimento di Elettronica e Informazione and IU.NET,Politecnico di Milano,Milano,Italy School of Integrated Circuits,Huazhong University of Science and Technology,Wuhan 430074,ChinaDepartment of Applied Physics,Hong Kong Polytechnic University,Hong Kong,China School of Integrated Circuits,Huazhong University of Science and Technology,Wuhan 430074,China

电子信息工程

complementary memtransistorneuromorphic computingreward-modulated spike timing-dependent plasticityremote supervise methodin-sensor computing

《Journal of Semiconductors》 2024 (006)

P.64-80 / 17

supported by the National Key Research and Development Program of China(No.2023YFB4502200);Natural Science Foundation of China(Nos.92164204 and 62374063);the Science and Technology Major Project of Hubei Province(No.2022AEA001).

10.1088/1674-4926/23120051

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