Designed Memristor Circuit for Self‐Limited Analog Switching and its Application to a Memristive Neural Network. (28th March 2019)

• Record Type: Journal Article
• Authors: Song, Hanchan; Kim, Young Seok; Park, Juseong; Kim, Kyung Min
• Is Part Of: Advanced Electronic Materials. Volume 5:Number 6(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-03-28
• Subjects: analog programming -- memristors -- neuromorphic devices -- self‐limited switching -- stateful logic; Materials -- Electric properties -- Periodicals; Materials science -- Periodicals; Magnetic materials -- Periodicals; Electronic apparatus and appliances -- Periodicals; 537
• DOI: 10.1002/aelm.201800740
• Languages: English
• ISSNs: 2199-160X

Abstract

Abstract: Analog memristors enable compact neuromorphic computing with low power consumption. One of the issues with the technology is slow precise analog data programming. In this study, a novel analog data programming method utilizing a self‐limited set switching is proposed. The method can transfer any resistance values from reference resistors to the target memristor accurately inside a crossbar array by performing an appropriate voltage clocking. An ideal memristor model based on the method is proposed and a Ti‐doped NbOx charge trap memristor is evaluated as a promising candidate for applications. The characteristic error of the Ti‐doped NbOx memristor device is about 5% on average, compared to the ideal memristor, and configuring optimum parallel resistors in the circuit further improves this to 2.95%. The method is then applied to program a memristive neural network and this error is confirmed negligible; thus the proposed method is viable. Abstract : Accurate and simple analog data programming via in‐memory gate operation is proposed. With the help of series and parallel resistors, memristor programming can be self‐limited, which allows accurate and fast programming. The proposed method can be applied to the programming of a memristor‐based analog neural network.