Effect of Temperature on Analog Memristor in Neuromorphic Computing

Yifu Huang, Reed Hopkins, David Janosky, Ying Chen Chen, Yao Feng Chang, Jack C. Lee

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In this article, the influence of the temperature instability of resistive memory switching on potential neuromorphic computing applications is extensively studied using an Intel TaOx-based analog-type memristor as a synaptic weight modulator in a neural network. Evaluation results show that the effect of ambient temperature during training and interference can degrade the neural network's accuracy due to inefficient weight updates and inevitable resistance or conductance drifting. Our results provide additional insights into device-level physical models and simple circuit-level design guidance for potential hardware-based neuromorphic computing applications.

Original languageEnglish (US)
Pages (from-to)6102-6105
Number of pages4
JournalIEEE Transactions on Electron Devices
Issue number11
StatePublished - Nov 1 2022


  • Memristor
  • neural network
  • neuromorphic computing
  • resistive switching
  • tantalum oxide
  • temperature impact

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering


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