title: Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing.
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.
authors.affiliation: Department of Electronic Engineering, College of Engineering at Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea. ychangju@jbnu.ac.kr.
authors.forename: Changju
authors.initials: C
authors.lastname: Yang
authors.affiliation: Department of Electronic Engineering, College of Engineering at Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea. hskim@jbnu.ac.kr.
authors.forename: Hyongsuk
authors.initials: H
authors.lastname: Kim
keywords: anti-serial architecture; complimentary action; linearity; linearity weight programming; weight programming