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journal article·2021-05Open access

Giant Ferroelectric Resistance Switching Controlled by a Modulatory Terminal for Low‐Power Neuromorphic In‐Memory Computing

Fei Xue, Xin He, Zhenyu Wang, José Ramón Durán Retamal, Zheng Chai, Lingling Jing, Chenhui Zhang, Hui Fang, Yang Chai, Tao Jiang, Weidong Zhang, Husam N. Alshareef, Zhigang Ji, Lain‐Jong Li, Jr‐Hau He, Xixiang Zhang

Advanced Materials· Volume 33· Issue 21
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Source abstract

Abstract

Abstract Ferroelectrics have been demonstrated as excellent building blocks for high‐performance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in‐memory computing. Here, it is reported that the emerging van der Waals ferroelectric α‐In2Se3 can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistance‐switching ratio of heterosynaptic memristors above 103, which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α‐In2Se3 channel is responsible for the resistance switching at various paired terminals. The third terminal of α‐In2Se3 memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read‐energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α‐In2Se3 neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy‐efficient, brain‐inspired computing systems and logic‐in‐memory computers.
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Research context

Electronic materials

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Perovskite Materials and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Physical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955‐6900 Saudi Arabia

National Key Laboratory of Science and Technology on Micro/Nano Fabrication Shanghai Jiao Tong University Shanghai 200240 China

Computer, Electrical, and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955‐6900 Saudi Arabia

Department of Electronics and Electrical Engineering Liverpool John Moores University Liverpool L3 3AF UK

Computer Science Department Loughborough University Loughborough LE11 3TU UK

Department of Applied Physics The Hong Kong Polytechnic University Kowloon Hong Kong China

CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro‐Nano Energy and Sensor Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 100083 China

Department of Materials Science and Engineering University of New South Wales Kensington NSW 2052 Australia

Department of Materials Science and Engineering City University of Hong Kong Kowloon Hong Kong China

Funding

King Abdullah University of Science and Technology · City University of Hong Kong

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