Emerging nonvolatile memories (eNVMs) have demonstrated satisfactory accuracy on various applications in deep learning. Characterized by high density and low leakage power consumption, resistive random access memory (RRAM) becomes very attractive in synaptic devices for deep neural networks (DNNs). RRAM-based synaptic devices include both analog and discrete versions. Unlike analog RRAM synapses which suffer from nonlinearity, discrete but multistate RRAM synapses are better suited for neural network hardware implementation. In this article, the multistate operation in RRAM arrays has been proposed as a synaptic device for DNN inference. Four-state conductance has been achieved in HfOx-based RRAM synaptic arrays. The impact of total ionizing dose (TID) on the multistate behavior of HfOx-based RRAM is investigated by irradiating a one-transistor-one-resistor (1T1R) 64-kb array with CMOS peripheral decoding circuitry fabricated at the 90-nm technology node with Co-60 gamma rays (60Co γ -ray irradiation).

Original languageEnglish (US)
Article number9399504
Pages (from-to)756-761
Number of pages6
JournalIEEE Transactions on Nuclear Science
Issue number5
StatePublished - May 2021


  • Neural network
  • nonvolatile memory (NVM)
  • resistive random access memory (RRAM)
  • synaptic device
  • 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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