Two-Step Read Scheme in One-Selector and One-RRAM Crossbar-Based Neural Network for Improved Inference Robustness

Jiyong Woo, Shimeng Yu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Introducing a threshold switching selector in a resistive random access memory (RRAM) is essential for implementing a crossbar array that accurately accelerates neuromorphic computations. But, at an expense, a read voltage (Vread) to be used for inference tasks is inevitably boosted. Therefore, this brief shows the effect of the enlarged Vread on the stability of conductance states of the RRAM relevant to the inference robustness. The multiple conductance states of the analog RRAM achieved by a SPICE simulation are stable under consecutive 106 cycles of nominal Vread. However, each state of the one selector and one RRAM begins to be disturbed at 104 cycles due to the boosted Vread. More importantly, when a certain state exceeds to the next state due to the accumulated Vread stress, a classification accuracy of the neural network is significantly degraded. We, thus, introduce a two-step read scheme that separates the roles of turning on the selector and reading the states. As the selector is turned on rapidly with an additional large pulse, the following Vread can be lowered. As a result, the read disturbance is minimized, and the optimized two-step pulse scheme allows 106 MNIST images to be recognized with >95% accuracy in the neural network.

Original languageEnglish (US)
Article number8510826
Pages (from-to)5549-5553
Number of pages5
JournalIEEE Transactions on Electron Devices
Issue number12
StatePublished - Dec 2018


  • Inference robustness
  • neuromorphic computing
  • read disturbance
  • resistive random access memory (RRAM)
  • selector device

ASJC Scopus subject areas

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


Dive into the research topics of 'Two-Step Read Scheme in One-Selector and One-RRAM Crossbar-Based Neural Network for Improved Inference Robustness'. Together they form a unique fingerprint.

Cite this