Excellent weight-updating-linearity synapse memory cell for self-learning neuron MOS neural networks

Hideo Kosaka, Tadashi Shibata, Hiroshi Ishii, Tadahiro Ohm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

A new synapse cell circuit employing a floating-gate memory has been developed which is characterized by an excellent weight-updating linearity. Such a feature has been realized for the first time by employing a simple self-feedback regime in each cell circuit. The new cell is composed of only seven transistors and inherits the all advanced features of our old six-transistor cell [1], such as the standby--power free and dual polarity characteristics, thus making it fully compatible to the hardware learning architecture of the Neuron MOS neural network. The basic characteristics of the cell are demonstrated using test circuits fabricated by a double-polysilicon CMOS process.

Original languageEnglish
Title of host publicationTechnical Digest - International Electron Devices Meeting
Editors Anon
PublisherPubl by IEEE
Pages623-626
Number of pages4
ISBN (Print)0780314506
Publication statusPublished - 1993 Dec 1
EventProceedings of the 1993 IEEE International Electron Devices Meeting - Washington, DC, USA
Duration: 1993 Dec 51993 Dec 8

Publication series

NameTechnical Digest - International Electron Devices Meeting
ISSN (Print)0163-1918

Other

OtherProceedings of the 1993 IEEE International Electron Devices Meeting
CityWashington, DC, USA
Period93/12/593/12/8

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering
  • Materials Chemistry

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  • Cite this

    Kosaka, H., Shibata, T., Ishii, H., & Ohm, T. (1993). Excellent weight-updating-linearity synapse memory cell for self-learning neuron MOS neural networks. In Anon (Ed.), Technical Digest - International Electron Devices Meeting (pp. 623-626). (Technical Digest - International Electron Devices Meeting). Publ by IEEE.