Efficient learning and crossbar operations with atomically-thin 2-D material compound synapses

Ivan Sanchez Esqueda, Huan Zhao, Han Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number152133
JournalJournal of Applied Physics
Volume124
Issue number15
DOIs
StatePublished - Oct 21 2018

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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