Emerging Optical In-Memory Computing Sensor Synapses Based on Low-Dimensional Nanomaterials for Neuromorphic Networks

Shen, Zongjie, Zhao, Chun, Kang, Lixing, Sun, Yi, Liu, Yina, Mitrovic, Ivona Z ORCID: 0000-0003-4816-8905, Yang, Li, Lim, Eng Gee, Zeng, Zhongming and Zhao, Cezhou (2022) Emerging Optical In-Memory Computing Sensor Synapses Based on Low-Dimensional Nanomaterials for Neuromorphic Networks. ADVANCED INTELLIGENT SYSTEMS, 4 (9). p. 2100236.

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Abstract

<jats:sec><jats:label /><jats:p>Emerging optical synapses with in‐memory computing sensor (IMCS) performance are considered to be one of the most effective candidates to circumvent the bottleneck of the current Von Neumann structure while developing neuromorphic systems with higher effectiveness and lower energy consumption. Biomimetic properties of optical IMCS synapses in function and form indicate the higher requirements for utilized functional materials, such as stronger optical sensitivity and lower energy dissipation. Because of properties with high optical‐sensitivity efficiency and excellent electrical conductivity, low‐dimensional nanomaterials have received tremendous interest in modulating optical‐induced synaptic plasticity and emulating optical‐triggered neuromorphic activity of optical IMCS synapses. Herein, a comprehensive summary of optical IMCS synapses based on low‐dimensional nanomaterials is introduced systematically for the first time, including 0D, 1D, and 2D materials. In addition, the content of biomimetic synaptic characteristics, materials classification, operation mechanism, and neuromorphic applications of optical IMCS synapses based on low‐dimensional nanomaterials are also summarized in this work. At last, the challenges and outlook related to artificial optical IMCS synapses with low‐dimensional nanomaterials are provided.</jats:p></jats:sec>

Item Type:	Article
Uncontrolled Keywords:	artificial intelligence, in-memory computing sensors, low-dimensional nanomaterials, neuromorphic networks, optical synapses
Depositing User:	Symplectic Admin
Date Deposited:	11 Oct 2022 07:35
Last Modified:	23 Aug 2023 12:09
DOI:	10.1002/aisy.202100236
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3165311