A SPICE Model of Phase Change Memory for Neuromorphic Circuits
A 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....
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.95278-95287 |
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| creator | Chen, Xuhui Hu, Huifang Huang, Xiaoqing Cai, Weiran Liu, Ming Lam, Chung Lin, Xinnan Zhang, Lining Chan, Mansun |
| description | A 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'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. |
| doi_str_mv | 10.1109/ACCESS.2020.2995907 |
| format | Article |
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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'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.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.2995907</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Biological neural networks ; Biological system modeling ; Bionics ; Circuit design ; Circuits ; Convergence ; Crystallization ; Electric potential ; Electrodes ; Handwriting ; Integrated circuit modeling ; Mathematical model ; Modules ; Neuromorphic circuits ; Neuromorphics ; Phase change materials ; phase change memory ; Simulation ; SPICE model ; spike-time-dependent plasticity ; spiking neural networks ; Time dependence ; Topology ; Transient analysis ; Voltage</subject><ispartof>IEEE access, 2020, Vol.8, p.95278-95287</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-55e44540b0a99505cd6b21065372859bb48886133c9fc7c0dce5d5b702e5984f3</citedby><cites>FETCH-LOGICAL-c408t-55e44540b0a99505cd6b21065372859bb48886133c9fc7c0dce5d5b702e5984f3</cites><orcidid>0000-0002-3587-0183</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9097232$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,4010,27610,27900,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Chen, Xuhui</creatorcontrib><creatorcontrib>Hu, Huifang</creatorcontrib><creatorcontrib>Huang, Xiaoqing</creatorcontrib><creatorcontrib>Cai, Weiran</creatorcontrib><creatorcontrib>Liu, Ming</creatorcontrib><creatorcontrib>Lam, Chung</creatorcontrib><creatorcontrib>Lin, Xinnan</creatorcontrib><creatorcontrib>Zhang, Lining</creatorcontrib><creatorcontrib>Chan, Mansun</creatorcontrib><title>A SPICE Model of Phase Change Memory for Neuromorphic Circuits</title><title>IEEE access</title><addtitle>Access</addtitle><description>A 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'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.</description><subject>Biological neural networks</subject><subject>Biological system modeling</subject><subject>Bionics</subject><subject>Circuit design</subject><subject>Circuits</subject><subject>Convergence</subject><subject>Crystallization</subject><subject>Electric potential</subject><subject>Electrodes</subject><subject>Handwriting</subject><subject>Integrated circuit modeling</subject><subject>Mathematical model</subject><subject>Modules</subject><subject>Neuromorphic circuits</subject><subject>Neuromorphics</subject><subject>Phase change materials</subject><subject>phase change memory</subject><subject>Simulation</subject><subject>SPICE model</subject><subject>spike-time-dependent plasticity</subject><subject>spiking neural networks</subject><subject>Time dependence</subject><subject>Topology</subject><subject>Transient analysis</subject><subject>Voltage</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUE1rwkAQDaWFivUXeFnoOXb2K8leChJsK2gr2J6X3c2sRtS1Gz347xsbKZ3LzDzmvXm8JBlSGFEK6mlclpPlcsSAwYgpJRXkN0mP0UylXPLs9t98nwyaZgNtFS0k817yPCbLxbSckHmocEuCJ4u1aZCUa7NfIZnjLsQz8SGSdzzF0G6Hde1IWUd3qo_NQ3LnzbbBwbX3k6-XyWf5ls4-XqfleJY6AcUxlRKFkAIsmNYgSFdlllHIJM9ZIZW1oiiKjHLulHe5g8qhrKTNgaFUhfC8n0w73SqYjT7EemfiWQdT618gxJU28Vi7LWpUKHLrrOdCCeq95dQ4BF9BZQ3lF63HTusQw_cJm6PehFPct_Y1a00KAC54e8W7KxdD00T0f18p6EvuustdX3LX19xb1rBj1Yj4x1CgcsYZ_wFm-Hvz</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Chen, Xuhui</creator><creator>Hu, Huifang</creator><creator>Huang, Xiaoqing</creator><creator>Cai, Weiran</creator><creator>Liu, Ming</creator><creator>Lam, Chung</creator><creator>Lin, Xinnan</creator><creator>Zhang, Lining</creator><creator>Chan, Mansun</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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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'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.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.2995907</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-3587-0183</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Biological neural networks Biological system modeling Bionics Circuit design Circuits Convergence Crystallization Electric potential Electrodes Handwriting Integrated circuit modeling Mathematical model Modules Neuromorphic circuits Neuromorphics Phase change materials phase change memory Simulation SPICE model spike-time-dependent plasticity spiking neural networks Time dependence Topology Transient analysis Voltage |
| title | A SPICE Model of Phase Change Memory for Neuromorphic Circuits |
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