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Sensors 2016, 16(8), 1320; doi:10.3390/s16081320

Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing

Department of Electronic Engineering, College of Engineering at Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 18 July 2016 / Revised: 11 August 2016 / Accepted: 11 August 2016 / Published: 19 August 2016
(This article belongs to the Section Physical Sensors)
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Abstract

A linearized programming method of memristor-based neural weights is proposed. Memristor is known as an ideal element to implement a neural synapse due to its embedded functions of analog memory and analog multiplication. Its resistance variation with a voltage input is generally a nonlinear function of time. Linearization of memristance variation about time is very important for the easiness of memristor programming. In this paper, a method utilizing an anti-serial architecture for linear programming is proposed. The anti-serial architecture is composed of two memristors with opposite polarities. It linearizes the variation of memristance due to complimentary actions of two memristors. For programming a memristor, additional memristor with opposite polarity is employed. The linearization effect of weight programming of an anti-serial architecture is investigated and memristor bridge synapse which is built with two sets of anti-serial memristor architecture is taken as an application example of the proposed method. Simulations are performed with memristors of both linear drift model and nonlinear model. View Full-Text
Keywords: weight programming; complimentary action; anti-serial architecture; linearity weight programming; complimentary action; anti-serial architecture; linearity weight programming; complimentary action; anti-serial architecture; linearity weight programming; complimentary action; anti-serial architecture; linearity
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, C.; Kim, H. Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing. Sensors 2016, 16, 1320.

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