Abstract

Neuromorphic computing systems built with resistive random access memory (RRAM) are attractive solutions for implementing deep learning on-chip. In this work, the single event and cumulative radiation damage susceptibility of a 1-transistor-1-resistor (1T1R) synaptic array architecture is investigated for the inference stage of a neuromorphic system. A physics-based SPICE RRAM model is developed to simulate the analog switching behavior of HfOx-based RRAM devices. SPICE simulations using the RRAM model are performed to capture the effects of transient heavy ion strikes and experimental results obtained on GeSe based RRAM devices are used to model the effects of cumulative radiation dose. Error patterns are fed into the software simulation of a multilayer perceptron (MLP), a representative artificial neural network for MNIST handwritten digit recognition. Radiation effects on the inference accuracy are analyzed. The results of the analysis indicate that the RRAM-based neuromorphic computing system is highly resistant to transient single event effects (SEEs) due to the low cross-section. The system is also shown to be tolerant to multi Mrad levels of total ionizing and displacement damage dose.

Original languageEnglish (US)
JournalIEEE Transactions on Nuclear Science
DOIs
StateAccepted/In press - Jan 1 2018

Keywords

  • analog RRAM
  • Hafnium compounds
  • Ions
  • multilayer perceptron
  • neuromorphic computing
  • Neuromorphics
  • Resistance
  • single-event upset
  • SPICE
  • Switches
  • total ionizing dose
  • Transient analysis

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering

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