Training a Neural Network on Analog TaOx ReRAM Devices Irradiated With Heavy Ions: Effects on Classification Accuracy Demonstrated With CrossSim

R. B. Jacobs-Gedrim, D. R. Hughart, S. Agarwal, G. Vizkelethy, E. S. Bielejec, B. L. Vaandrager, S. E. Swanson, K. E. Knisely, J. L. Taggart, Hugh Barnaby, M. J. Marinella

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The image classification accuracy of a TaOx ReRAM-based neuromorphic computing accelerator is evaluated after intentionally inducing a displacement damage up to a fluence of 1014 2.5-MeV Si ions/cm2 on the analog devices that are used to store weights. Results are consistent with a radiation-induced oxygen vacancy production mechanism. When the device is in the high-resistance state during heavy ion radiation, the device resistance, linearity, and accuracy after training are only affected by high fluence levels. The findings in this paper are in accordance with the results of previous studies on TaOx-based digital resistive random access memory. When the device is in the low-resistance state during irradiation, no resistance change was detected, but devices with a 4-kΩ inline resistor did show a reduction in accuracy after training at 1014 2.5-MeV Si ions/cm2. This indicates that changes in resistance can only be somewhat correlated with changes to devices' analog properties. This paper demonstrates that TaOx devices are radiation tolerant not only for high radiation environment digital memory applications but also when operated in an analog mode suitable for neuromorphic computation and training on new data sets.

Original languageEnglish (US)
Article number8577027
Pages (from-to)54-60
Number of pages7
JournalIEEE Transactions on Nuclear Science
Volume66
Issue number1
DOIs
StatePublished - Jan 2019

Keywords

  • Analog
  • deep learning
  • neuromorphic accelerator
  • radiation effects
  • resistive random-access memory (ReRAM)
  • tantalum oxide

ASJC Scopus subject areas

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

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