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

• Published in: Advanced materials (Weinheim) 2021-05, Vol.33 (21), p.e2008709-n/a
• Main Authors: Xue, Fei, He, Xin, Wang, Zhenyu, Retamal, José Ramón Durán, Chai, Zheng, Jing, Lingling, Zhang, Chenhui, Fang, Hui, Chai, Yang, Jiang, Tao, Zhang, Weidong, Alshareef, Husam N., Ji, Zhigang, Li, Lain‐Jong, He, Jr‐Hau, Zhang, Xixiang
• Format: Article
• Language: English
• Subjects: Boolean algebra; Circuits; Computation; Computer memory; Energy consumption; Ferroelectric materials; Ferroelectricity; Ferroelectrics; heterosynaptic plasticity; in‐memory computing; Materials science; Memristors; neuromorphic computing; Object recognition; Switching; Synapses; Unsupervised learning; van der Waals ferroelectric
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.202008709

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. Ferroelectric switching in the van der Waals ferroelectric α‐In2Se3 is used to achieve a resistance‐switching ratio of heterosynaptic memristors above 103. The α‐In2Se3 heterosynaptic memristors feature picojoule read‐energy consumption. 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 devices can also realize in‐memory computing.