Neuromorphic computing systems built with resistive random access memory (RRAM) are attractive solutions for implementing deep learning on-chip. In this paper, the single event and cumulative radiation damage susceptibility of a 1-transistor-1-resistor 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, 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 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)
Article number8576612
Pages (from-to)97-103
Number of pages7
JournalIEEE Transactions on Nuclear Science
Issue number1
StatePublished - Jan 2019


  • Analog resistive random access memory (RRAM)
  • multilayer perceptron (MLP)
  • neuromorphic computing
  • single-event upset (SEU)
  • total ionizing dose (TID)

ASJC Scopus subject areas

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


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