Work

Perspective: Entropy-stabilized oxide memristors

Year: 2024

Type: article

Abstract: A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of “task specific” neural networks that derive from this material tunability.

Source: Applied Physics Letters

DOI: 10.1063/5.0216429

Authors: Sieun Chae, Sangmin Yoo, Emmanouil Kioupakis, Wei D. Lu, John T. Heron

Institutions: Oregon State University, University of Michigan

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: MRSEC: Center for Nanoscale Science