A Compact Fully Ferroelectric-FETs Reservoir Computing Network With Sub-100 ns Operating Speed

• Published in: IEEE electron device letters Vol. 43; no. 9; pp. 1555 - 1558
• Main Authors: Tang, Mingfeng, Zhan, Xuepeng, Wu, Shuhao, Bai, Maoying, Feng, Yang, Zhao, Guoqing, Wu, Jixuan, Chai, Junshuai, Xu, Hao, Wang, Xiaolei, Chen, Jiezhi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: FeFETs; Ferroelectric FET; Ferroelectric materials; Ferroelectricity; Field effect transistors; full-connection; HZO; Performance evaluation; reservoir computing; Reservoirs; Semiconductor devices; Software packages; Switches; Task analysis; Training
• ISSN: 0741-3106; 1558-0563
• DOI: 10.1109/LED.2022.3188496

Reservoir computing (RC) is a low-cost and temporary-signal friendly computational framework, whose hardware implementation is hindered by integrating huge amounts and various kinds of devices. Benefitted from the logic in memory (LIM) capability, the process compatible Hf 0.5 Zr 0.5 O 2 (HZO)-based ferroelectric field-effect-transistor (FeFET) is a promising candidate for implementing artificial networks. In this letter, a fully FeFETs RC network is proposed. Multiple functions can be achieved in a single device owing to its intrinsic characteristics, and the richness of virtual nodes is largely enhanced through full-connection structures. Impressively, only 44 FeFETs are required to construct a compact RC network with 100 ns operating speed and high accuracy in classification tasks. This paves the way to develop the high energy-efficiency FeFET RC networks.