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Simple square pulses for implementing spike‐timing‐dependent plasticity in phase‐change memory
Author(s) -
Zhong Yingpeng,
Li Yi,
Xu Lei,
Miao Xiangshui
Publication year - 2015
Publication title -
physica status solidi (rrl) – rapid research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.786
H-Index - 68
eISSN - 1862-6270
pISSN - 1862-6254
DOI - 10.1002/pssr.201510150
Subject(s) - neuromorphic engineering , spike (software development) , phase change memory , spike timing dependent plasticity , synaptic weight , computer science , dissipation , phase (matter) , spiking neural network , phase change , square (algebra) , plasticity , power consumption , synaptic plasticity , materials science , artificial neural network , power (physics) , layer (electronics) , artificial intelligence , physics , nanotechnology , chemistry , mathematics , receptor , software engineering , engineering physics , biochemistry , quantum mechanics , thermodynamics , composite material , geometry
Phase‐change memory (PCM) is a promising candidate as an artificial synapse. A compact operation method to implement synaptic functions with low power consumption is critical for constructing large‐scale neuromorphic system. Here we propose a square spike strategy for implementing spike‐timing‐dependent plasticity (STDP) in PCM. Based on the heat accumulation effect in PCM, modulating the time intervals of pre‐ and post‐spikes results in different heat generation and dissipation conditions, which lead to various crystalline/ amorphous ratios in the phase change material layer in devices with diverse synaptic weights. Four forms of STDP learning rule are experimentally demonstrated. This study will further promote the development of PCM technology involved in neuromorphic systems. (© 2015 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)