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Designed Memristor Circuit for Self‐Limited Analog Switching and its Application to a Memristive Neural Network
Author(s) -
Song Hanchan,
Kim Young Seok,
Park Juseong,
Kim Kyung Min
Publication year - 2019
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.201800740
Subject(s) - memristor , neuromorphic engineering , memistor , resistor , artificial neural network , electronic engineering , crossbar switch , computer science , resistive random access memory , voltage , materials science , electrical engineering , artificial intelligence , engineering
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 NbO x charge trap memristor is evaluated as a promising candidate for applications. The characteristic error of the Ti‐doped NbO x 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.