Work

2D Memory Transistors for Neuromorphic Computing

Year: 2026

Type: article

Abstract: The boom in artificial intelligence has driven in-memory computing for high speed and power efficiency. Motivated by the need, neuromorphic computing (NC)—mimicking biological spike neural networks—ies a promising alternative that requires artificial neurons and synapses to construct NC circuits. 2D memory transistors have received intensive interest in recent research for NC applications as both artificial neurons and synapses with additional advantages for high device density and energy efficiency. This review intends to highlight recent progress made in the exploration of 2D memory transistors and their implementation into electrical and optoelectronic NC circuits. Discussions of remaining challenges and future perspectives are also provided.

Source: Materials

DOI: 10.3390/ma19214710

Authors: Judy Wu, Andrew Shultz, Saad Alzahrani, Ryan Goul

Institution: University of Kansas

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Science Foundation