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Analytical Modeling of Organic–Inorganic CH 3 NH 3 PbI 3 Perovskite Resistive Switching and its Application for Neuromorphic Recognition
Author(s) -
Ren Yanyun,
Milo Valerio,
Wang Zhongqiang,
Xu Haiyang,
Ielmini Daniele,
Zhao Xiaoning,
Liu Yichun
Publication year - 2018
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.201700035
Subject(s) - neuromorphic engineering , materials science , perovskite (structure) , memristor , protein filament , radius , resistor , resistive random access memory , reset (finance) , optoelectronics , artificial neural network , resistive touchscreen , synapse , electronic engineering , computer science , chemical engineering , electrical engineering , voltage , artificial intelligence , composite material , engineering , neuroscience , computer security , financial economics , economics , biology
This paper presents an analytical model of resistive switching in organic–inorganic CH 3 NH 3 PbI 3 perovskite. It is interpreted that the resistive switching phenomenon is due to the formation/rupture of iodine vacancy‐based conductive filament (CF) propagating in both vertical and lateral directions. Set and reset processes are explained in the model by the evolution of the CF length and radius driven by electrical and thermal forces. The model‐based simulation results can describe the experimental results, providing an estimate of several switching parameters such as the activation energy of iodine vacancy migration and the CF diameter. Learning in a two‐transistor/one‐resistor synapse structure is demonstrated by simulations. Finally, the neuromorphic recognition of multiple patterns is demonstrated through a two‐layer neural network consisting of 5625 presynaptic neurons and four postsynaptic neurons.