Effect of weight overlap region on neuromorphic system with memristive synaptic devices

Geun Ho Lee, Tae Hyeon Kim, Min Suk Song, Jinwoo Park, Sungjoon Kim, Kyungho Hong, Yoon Kim, Byung Gook Park, Hyungjin Kim

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Recently, hardware-based neural network using memristive devices, so called neuromorphic system, has been extensively studied. Especially, on-chip (in situ) learning methods where training occurs inside hardware structure itself have been proposed and optimized based on memristor crossbar arrays regarding the linearity of weight-update characteristics. In this study, we analyze the effect of conductance overlap region of memristor on the recognition accuracy for on-chip learning simulation. The effect of conductance overlap region on recognition accuracy for modified national institute of standards and technology (MNIST) dataset is studied with an identical potentiation/depression pulse applied to Pt/Al2O3/TiOx/Ti/Pt stacked memristor. The overlap range can be varied by different pulse amplitude, and the training characteristics of memristive neural network is significantly dependent on the weight-update overlap region.

Original languageEnglish
Article number111999
JournalChaos, Solitons and Fractals
StatePublished - Apr 2022


  • Memristor
  • Neural network
  • Neuromorphic system
  • On-chip learning
  • Weight overlap region


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