TitleA SPICE Model of Phase Change Memory for Neuromorphic Circuits
AuthorsChen, Xuhui
Hu, Huifang
Huang, Xiaoqing
Cai, Weiran
Liu, Ming
Lam, Chung
Lin, Xinnan
Zhang, Lining
Chan, Mansun
AffiliationPeking Univ, Shenzhen Key Lab Adv Elect Device & Integrat, ECE, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
Shenzhen Univ, Inst Microscale Optoelect, Shenzhen 518061, Peoples R China
Chinese Acad Sci, Inst Microelect, Key Lab Microelect Devices & Integrat Technol, Beijing 100029, Peoples R China
Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Jiangsu Adv Memory Technol Corp Ltd, Huaian 223302, Peoples R China
Peking Univ, Sch Elect & Comp Engn, Shenzhen 518055, Peoples R China
HKUST, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China
Issue Date2020
AbstractA phase change memory (PCM) model suitable for neuromorphic circuit simulations is developed. A crystallization ratio module is used to track the memory state in the SET process, and an active region radius module is developed to track the continuously varying amorphous region in the RESET process. To converge the simulations with bi-stable memory states, a predictive filament module is proposed using a previous state in iterations of nonlinear circuit matrix under a voltage-driven mode. Both DC and transient analysis are successfully converged in circuits with voltage sources. The spiking-time-dependent-plasticity (STDP) characteristics essential for synaptic PCM are successfully reproduced with SPICE simulations verifying the model & x2019;s promising applications in neuromorphic circuit designs. Further on, the developed PCM model is applied to propose a neuron circuit topology with lateral inhibitions which is more bionic and capable of distinguishing fuzzy memories. Finally, unsupervised learning of handwritten digits on neuromorphic circuits is simulated to verify the integrity of models in a large-scale-integration circuits. For the first time in literature an emerging memory model is developed and applied successfully in neuromorphic circuit designs, and the model is applicable to flexible designs of neuron circuits for further performance improvements.
Appears in Collections:信息工程学院

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