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Neuromorphic Computing: Designed Memristor Circuit for Self‐Limited Analog Switching and its Application to a Memristive Neural Network (Adv. Electron. Mater. 6/2019)
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.201970032
Subject(s) - neuromorphic engineering , memristor , crossbar switch , memistor , artificial neural network , resistor , resistive random access memory , computer science , spiking neural network , electronic engineering , materials science , voltage , computer hardware , electrical engineering , artificial intelligence , engineering
In article number 1800740 , H. Song et al. propose an analog data‐programming method for memristors via in‐memory operation in a crossbar array. Analog data can be transferred from a selected reference resistor cell to the target memristor cell by appropriate voltage clocking. Such data transfer can be triggered at any location in the array accurately and quickly; optimization of the design gives a characteristic error of just 2.95%. Their method is then applied to program a memristive neural network and the error is confirmed to be negligible, suggesting this setup can be used in neuromorphic computing applications.