Multi-Terminal Memristive Devices Enabling Tunable Synaptic Plasticity in Neuromorphic Hardware: A Mini-Review

Frontiers in Nanotechnology

Fabien Alibart²  Yann Beilliard³ 

[1] Institut Interdisciplinaire d′Innovation Technologique (3IT), Université de Sherbrooke, Boulevard de l'Université, Sherbrooke, QC, Canada;Institute of Electronics, Microelectronics and Nanotechnology (IEMN), Université de Lille, Villeneuve d'Ascq, France;Laboratoire Nanotechnologies Nanosystèmes (LN2)–CNRS, Université de Sherbrooke, Boulevard de l’Université, Sherbrooke, QC, Canada;

关键词: memristive devices;    artificial synapse;    heterosynaptic plasticity;    synaptic interactions;    neuromorphic computing;    2D materials;   

DOI  :  10.3389/fnano.2021.779070

来源: DOAJ

【 摘 要 】

Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in computational neuroscience have demonstrated the importance of heterosynaptic plasticity for network activity regulation and memorization. Implementing heterosynaptic plasticity in hardware is thus highly desirable, but important materials and engineering challenges remain, calling for breakthroughs in neuromorphic devices. In this mini-review, we propose an overview of the latest advances in multi-terminal memristive devices on silicon with tunable synaptic plasticity, enabling heterosynaptic plasticity in hardware. The scalability and compatibility of the devices with industrial complementary metal oxide semiconductor (CMOS) technologies are discussed.

【 授权许可】

Unknown