Ionizing Radiation Effects in SONOS-Based Neuromorphic Inference Accelerators

T. Patrick Xiao, Christopher H. Bennett, Sapan Agarwal, David R. Hughart, Hugh J. Barnaby, Helmut Puchner, Venkatraman Prabhakar, A. Alec Talin, Matthew J. Marinella

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We evaluate the sensitivity of neuromorphic inference accelerators based on silicon-oxide-nitride-oxide-silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects. Data retention statistics were collected for 16 Mbit of 40-nm SONOS digital memory exposed to ionizing radiation from a Co-60 source, showing good retention of the bits up to the maximum dose of 500 krad(Si). Using this data, we formulate a rate-equation-based model for the TID response of trapped charge carriers in the ONO stack and predict the effect of TID on intermediate device states between 'program' and 'erase.' This model is then used to simulate arrays of low-power, analog SONOS devices that store 8-bit neural network weights and support in situ matrix-vector multiplication. We evaluate the accuracy of the irradiated SONOS-based inference accelerator on two image recognition tasks - CIFAR-10 and the challenging ImageNet data set - using state-of-the-art convolutional neural networks, such as ResNet-50. We find that across the data sets and neural networks evaluated, the accelerator tolerates a maximum TID between 10 and 100 krad(Si), with deeper networks being more susceptible to accuracy losses due to TID.

Original languageEnglish (US)
Article number9353047
Pages (from-to)762-769
Number of pages8
JournalIEEE Transactions on Nuclear Science
Issue number5
StatePublished - May 2021


  • Charge trap memory
  • inference accelerators
  • ionizing radiation
  • neural networks
  • neuromorphic computing
  • silicon-oxide-nitride-oxide-silicon (SONOS)
  • 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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