IEEE Journal of the Electron Devices Society (Jan 2022)

NOR-Type 3-D Synapse Array Architecture Based on Charge-Trap Flash Memory

  • Jung Nam Kim,
  • Jaehong Lee,
  • Jo Eun Kim,
  • Suck Won Hong,
  • Minsuk Koo,
  • Yoon Kim

DOI
https://doi.org/10.1109/JEDS.2022.3208241
Journal volume & issue
Vol. 10
pp. 813 – 820

Abstract

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In this work, we proposed a three-dimensional (3-D) channel stacked array architecture based on charge-trap flash (CTF) memory for an artificial neural network accelerator. The proposed synapse array architecture could be a promising solution for implementing efficiently a large-size artificial neural network on a limited-size hardware chip. We designed a full array architecture including a stacked layer selection circuit. In addition, we investigated the synaptic characteristics of CTF device by using technology computer-aided design (TCAD) simulation. We demonstrated the feasibility of the synapse array for neural network accelerators through a system-level MATLAB simulation with the Modified National Institute of Standards and Technology (MNIST) database.

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