Photoresponsive Two-Dimensional Magnetic Junctions for Reconfigurable In-Memory Sensing.

ACS Nano

Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, Beijing 100084,China.

Published: October 2024

AI Article Synopsis

  • Magnetic tunneling junctions (MTJs) are key in magnetic random access memory and can potentially simplify hardware while lowering power use, but they lack sensitivity to light, which restricts their use in AI-related in-memory sensing.
  • The new all-two-dimensional (2D) magnetic junction FeGaTe/WSe/FeGaTe combines 2D magnetism with optical sensing, allowing the tunneling barrier to be optically tuned thanks to the properties of WSe.
  • This development enables flexible control of junction states and magnetoresistance through light intensity, facilitating the creation of a high-performance machine vision system for image recognition that enhances the functionality of magnetic memory in AI applications.

Article Abstract

Magnetic tunneling junctions (MTJs) lie in the core of magnetic random access memory, holding promise in integrating memory and computing to reduce hardware complexity, transition latency, and power consumption. However, traditional MTJs are insensitive to light, limiting their functionality in in-memory sensing─a crucial component for machine vision systems in artificial intelligence applications. Herein, the convergence of magnetic memory with optical sensing capabilities is achieved in the all-two-dimensional (2D) magnetic junction FeGaTe/WSe/FeGaTe, which combines 2D magnetism and optoelectronic properties. The clean intrinsic band gap and prominent photoresponse of interlayer WSe endow the tunneling barrier with optical tunability. The on-off states of junctions and the magnetoresistance can be flexibly controlled by the intensity of the optical signal at room temperature. Based on the optical-tunable magnetoresistance in all-2D magnetic junctions, a machine vision system with the architecture of in-memory sensing and computing is constructed, which possesses high performance in image recognition. Our work exhibits the advantages of 2D magneto-electronic devices and extends the application scenarios of magnetic memory devices in artificial intelligence.

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Source
http://dx.doi.org/10.1021/acsnano.4c09735DOI Listing

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