Switched diffusion analog memory for neural networks with hebbian learning function and its linear operation

Hyosig Won, Yoshihiro Hayakawa, Koji Nakajima, Yasuji Sawada

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


We have fabricated a new analog memory for integrated artificial neural networks. Several attempts have been made to develop a linear characteristics of floating-gate analog memorys with feedback circuits. The learning chip has to have a large number of learning control circuit. In this paper, we propose a new analog memory SDAM with three cascaded TFTs. The new analog memory has a simple design, a small area occupancy, a fast switching speed and an accurate linearity. To improve accurate linearity, we propose a new charge transfer process. The device has a tunnel junction (poly-Si/poly-Si oxide/poly-Si sandwich structure), a thin-film transistor, two capacitors, and a floating-gate MOSFET. The diffusion of the charges injected through the tunnel junction are controlled by a source follower operation of a thin film transistor (TFT). The proposed operation is possible that the amounts of transferred charges are constant independent of the charges in storage capacitor.

Original languageEnglish
Pages (from-to)746-751
Number of pages6
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number6
Publication statusPublished - 1996 Jun 1
EventProceedings of the 1995 Joint Technical Conference on Circuits/Systems, Computers and Communications (JTC-CSCC'95) - Kanazawa, Jpn
Duration: 1996 Sep 181996 Sep 21

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics


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