Shape‐Dependent Multi‐Weight Magnetic Artificial Synapses for Neuromorphic Computing

Leonard, Thomas; Liu, Samuel; Alamdar, Mahshid; Jin, Harrison; Cui, Can; Akinola, Otitoaleke G.; Xue, Lin; Xiao, T. Patrick; Friedman, Joseph S.; Marinella, Matthew J.; Bennett, Christopher H.; Incorvia, Jean Anne C.

doi:10.1002/aelm.202200563

Title: Shape‐Dependent Multi‐Weight Magnetic Artificial Synapses for Neuromorphic Computing

Journal Article · Sun Sep 11 00:00:00 EDT 2022 · Advanced Electronic Materials

DOI:https://doi.org/10.1002/aelm.202200563· OSTI ID:1995870

Leonard, Thomas ^[1]; Liu, Samuel ^[1]; Alamdar, Mahshid ^[1];

^[1]; Cui, Can ^[1]; Akinola, Otitoaleke G. ^[1]; Xue, Lin ^[2]; Xiao, T. Patrick ^[3]; Friedman, Joseph S. ^[4]; Marinella, Matthew J. ^[3]; Bennett, Christopher H. ^[3];

^[1]

Electrical and Computer Engineering Department University of Texas at Austin Austin TX 78712 USA
Applied Materials Santa Clara CA 95054 USA
Sandia National Laboratories Albuquerque NM 87123 USA
Electrical and Computer Engineering Department University of Texas at Dallas Richardson TX 75080 USA

Abstract In neuromorphic computing, artificial synapses provide a multi‐weight (MW) conductance state that is set based on inputs from neurons, analogous to the brain. Herein, artificial synapses based on magnetic materials that use a magnetic tunnel junction (MTJ) and a magnetic domain wall (DW) are explored. By fabricating lithographic notches in a DW track underneath a single MTJ, 3–5 stable resistance states that can be repeatably controlled electrically using spin‐orbit torque are achieved. The effect of geometry on the synapse behavior is explored, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a rectangular device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application‐specific synaptic functions. Implementing an artificial neural network (NN) applied to streamed Fashion‐MNIST data, the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional NN for CIFAR‐100 image recognition, the rectangular magnetic synapse achieves near‐ideal inference accuracy, due to the stability of its resistance levels. This work shows MW magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.

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Sponsoring Organization:: USDOE

OSTI ID:: 1995870

Journal Information:: Advanced Electronic Materials, Journal Name: Advanced Electronic Materials Vol. 8 Journal Issue: 12; ISSN 2199-160X

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United States

Language:: English

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