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Associative Memory for Image Recovery with a High‐Performance Memristor Array
Author(s) -
Zhou Ying,
Wu Huaqiang,
Gao Bin,
Wu Wei,
Xi Yue,
Yao Peng,
Zhang Shuanglin,
Zhang Qingtian,
Qian He
Publication year - 2019
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.201900155
Subject(s) - memristor , memistor , resistive random access memory , content addressable memory , computer science , materials science , reliability (semiconductor) , image (mathematics) , asynchronous communication , bidirectional associative memory , artificial neural network , neuromorphic engineering , computer hardware , electronic engineering , artificial intelligence , electrical engineering , voltage , power (physics) , engineering , computer network , physics , quantum mechanics
Associative memory is one of the significant characteristics of the biological brain. However, it has yet to be realized in a large memristor array due to the high requirements on the memristor device. In this work, the multilevel memristor cell is optimized by employing an electro‐thermal modulation layer. Memristor devices show both high resistance, cell‐to‐cell uniformity, and multilevel resistive switching behaviors with good reliability. A Hopfield neural network is experimentally demonstrated on a 1k memristor array that is capable of realizing the associative memory function for emotion image recovery. By using both asynchronous and synchronous refresh schemes, complete emotion images can be recalled from partial information.