Synaptic Device with High Rectification Ratio Resistive Switching and Its Impact on Spiking Neural Network

Chae Soo Kim, Taehyung Kim, Kyung Kyu Min, Yeonwoo Kim, Sungjun Kim, Byung Gook Park

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We propose self-rectifying resistive random access memory (RRAM) synapse to prevent reverse leakage current problem which occurs when RRAM is integrated with integrate and fire (IF) circuit in spiking neural network (SNN). Ni/W/SiNx/n-Si RRAM was fabricated by varying the bottom electrode (BE) doping concentration and their rectifying characteristics were analyzed. Low BE doping concentration device showed self-rectifying characteristics without any additional selector or diode device. Furthermore, hardware-based system-level simulation was conducted to evaluate the effect of self-rectifying RRAM synapse on MNIST classification accuracy. About 93.34% accuracy was obtained using the proposed RRAM.

Original languageEnglish
Article number9366932
Pages (from-to)1610-1615
Number of pages6
JournalIEEE Transactions on Electron Devices
Volume68
Issue number4
DOIs
StatePublished - Apr 2021

Keywords

  • Neuromorphic
  • resistive random access memory (RRAM)
  • self-rectifying
  • synaptic device
  • system-level simulation

Fingerprint

Dive into the research topics of 'Synaptic Device with High Rectification Ratio Resistive Switching and Its Impact on Spiking Neural Network'. Together they form a unique fingerprint.

Cite this