Accurate and efficient synaptic weight programming and vector-matrix multiplication are demonstrated using compound synapses constructed with ultralow power binary memristive devices having oxidized atomically thin two-dimensional hexagonal boron nitride (BNOx) filament formation layers. Experimental data of the resistive-switching current-voltage characteristics of BNOx memristors are used to formulate variation-aware models that enable statistically analyzing the trade-off between efficiency and accuracy as a function of the synaptic resolution (i.e., levels of synaptic weight programming). Results are compared with commonly reported oxide-based memristors indicating orders of magnitude (i.e., ∼105) improvements in power efficiency and ∼2-5× improvements in accuracy.
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Efficient learning and crossbar operations with atomically-thin 2-D material compound synapses
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21 October 2018
Research Article|
October 16 2018
Efficient learning and crossbar operations with atomically-thin 2-D material compound synapses
Ivan Sanchez Esqueda;
Ivan Sanchez Esqueda
a)
1
Information Sciences Institute, University of Southern California
, Marina del Rey, California 90292, USA
a)Authors to whom correspondence should be addressed: isanchez@isi.edu. Tel.: 310-448-8238 and han.wang.4@usc.edu
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Huan Zhao;
Huan Zhao
2
Ming Hsieh Department of Electrical Engineering, University of Southern California
, Los Angeles, California 90089, USA
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Han Wang
Han Wang
a)
2
Ming Hsieh Department of Electrical Engineering, University of Southern California
, Los Angeles, California 90089, USA
a)Authors to whom correspondence should be addressed: isanchez@isi.edu. Tel.: 310-448-8238 and han.wang.4@usc.edu
Search for other works by this author on:
a)Authors to whom correspondence should be addressed: isanchez@isi.edu. Tel.: 310-448-8238 and han.wang.4@usc.edu
J. Appl. Phys. 124, 152133 (2018)
Article history
Received:
June 01 2018
Accepted:
August 20 2018
Citation
Ivan Sanchez Esqueda, Huan Zhao, Han Wang; Efficient learning and crossbar operations with atomically-thin 2-D material compound synapses. J. Appl. Phys. 21 October 2018; 124 (15): 152133. https://doi.org/10.1063/1.5042468
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