A Leaky Integrate-and-Fire Neuron Based on Hexagonal Boron Nitride (h-BN) Monocrystalline Memristor
As a competitive candidate for artificial neurons, memristors have become the focus of intense research owing to their intrinsic ion migration tunability, enabling an authentic implementation of biomimicry. However, they still suffer from variability issues due to 3-D uncontrollable filament dynamic...
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| Veröffentlicht in: | IEEE transactions on electron devices 2022-11, Vol.69 (11), p.6049-6056 |
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| creator | Qian, Fangsheng Chen, Ruo-Si Wang, Ruopeng Wang, Junjie Xie, Peng Mao, Jing-Yu Lv, Ziyu Ye, Shenghao Yang, Jia-Qin Wang, Zhanpeng Zhou, Ye Han, Su-Ting |
| description | As a competitive candidate for artificial neurons, memristors have become the focus of intense research owing to their intrinsic ion migration tunability, enabling an authentic implementation of biomimicry. However, they still suffer from variability issues due to 3-D uncontrollable filament dynamics in an amorphous medium and modeling of switching dynamics underlying filament growth and rupture is still under investigation. In this work, we present volatile memristors that exhibit desired characteristics for neuromorphic computing with low performance variations utilizing a hexagonal boron nitride (h-BN) monocrystalline as a switching medium. Theoretical investigations assisted by the Monte Carlo simulation combined with experimentally detected {I} - {V} characteristics described that the electric field dominates the set process, whereas the Gibbs-Thomson interfacial energy minimization and heat dissipation influence the relaxation process mostly. Additionally, h-BN memristors with high switching uniformity provide an ideal hardware platform for credible neuron emulation and software identification of digital images. |
| doi_str_mv | 10.1109/TED.2022.3206170 |
| format | Article |
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However, they still suffer from variability issues due to 3-D uncontrollable filament dynamics in an amorphous medium and modeling of switching dynamics underlying filament growth and rupture is still under investigation. In this work, we present volatile memristors that exhibit desired characteristics for neuromorphic computing with low performance variations utilizing a hexagonal boron nitride (h-BN) monocrystalline as a switching medium. Theoretical investigations assisted by the Monte Carlo simulation combined with experimentally detected <inline-formula> <tex-math notation="LaTeX">{I} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">{V} </tex-math></inline-formula> characteristics described that the electric field dominates the set process, whereas the Gibbs-Thomson interfacial energy minimization and heat dissipation influence the relaxation process mostly. Additionally, h-BN memristors with high switching uniformity provide an ideal hardware platform for credible neuron emulation and software identification of digital images.]]></description><identifier>ISSN: 0018-9383</identifier><identifier>EISSN: 1557-9646</identifier><identifier>DOI: 10.1109/TED.2022.3206170</identifier><identifier>CODEN: IETDAI</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Boron ; Boron nitride ; Digital imaging ; Electric fields ; Electrodes ; Hexagonal boron nitride (h-BN) ; Interfacial energy ; Ion migration ; Memristors ; monocrystalline ; Monte Carlo simulation ; Neurons ; Performance evaluation ; spiking neural network (SNN) ; Switches ; Switching ; Threshold voltage</subject><ispartof>IEEE transactions on electron devices, 2022-11, Vol.69 (11), p.6049-6056</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-30bb8205937e54eabd7ce73a11a7c68646709e31a88f19a40f285356f55213783</citedby><cites>FETCH-LOGICAL-c291t-30bb8205937e54eabd7ce73a11a7c68646709e31a88f19a40f285356f55213783</cites><orcidid>0000-0003-3392-7569</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9903409$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9903409$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Qian, Fangsheng</creatorcontrib><creatorcontrib>Chen, Ruo-Si</creatorcontrib><creatorcontrib>Wang, Ruopeng</creatorcontrib><creatorcontrib>Wang, Junjie</creatorcontrib><creatorcontrib>Xie, Peng</creatorcontrib><creatorcontrib>Mao, Jing-Yu</creatorcontrib><creatorcontrib>Lv, Ziyu</creatorcontrib><creatorcontrib>Ye, Shenghao</creatorcontrib><creatorcontrib>Yang, Jia-Qin</creatorcontrib><creatorcontrib>Wang, Zhanpeng</creatorcontrib><creatorcontrib>Zhou, Ye</creatorcontrib><creatorcontrib>Han, Su-Ting</creatorcontrib><title>A Leaky Integrate-and-Fire Neuron Based on Hexagonal Boron Nitride (h-BN) Monocrystalline Memristor</title><title>IEEE transactions on electron devices</title><addtitle>TED</addtitle><description><![CDATA[As a competitive candidate for artificial neurons, memristors have become the focus of intense research owing to their intrinsic ion migration tunability, enabling an authentic implementation of biomimicry. However, they still suffer from variability issues due to 3-D uncontrollable filament dynamics in an amorphous medium and modeling of switching dynamics underlying filament growth and rupture is still under investigation. In this work, we present volatile memristors that exhibit desired characteristics for neuromorphic computing with low performance variations utilizing a hexagonal boron nitride (h-BN) monocrystalline as a switching medium. Theoretical investigations assisted by the Monte Carlo simulation combined with experimentally detected <inline-formula> <tex-math notation="LaTeX">{I} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">{V} </tex-math></inline-formula> characteristics described that the electric field dominates the set process, whereas the Gibbs-Thomson interfacial energy minimization and heat dissipation influence the relaxation process mostly. Additionally, h-BN memristors with high switching uniformity provide an ideal hardware platform for credible neuron emulation and software identification of digital images.]]></description><subject>Boron</subject><subject>Boron nitride</subject><subject>Digital imaging</subject><subject>Electric fields</subject><subject>Electrodes</subject><subject>Hexagonal boron nitride (h-BN)</subject><subject>Interfacial energy</subject><subject>Ion migration</subject><subject>Memristors</subject><subject>monocrystalline</subject><subject>Monte Carlo simulation</subject><subject>Neurons</subject><subject>Performance evaluation</subject><subject>spiking neural network (SNN)</subject><subject>Switches</subject><subject>Switching</subject><subject>Threshold voltage</subject><issn>0018-9383</issn><issn>1557-9646</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1PAjEQxRujiYjeTbw08aKHYj-37VEQhATwguem7M7iImyxXRL5710C8TQzmfcmb34I3TPaY4zal8Xwrccp5z3BacY0vUAdppQmNpPZJepQygyxwohrdJPSuh0zKXkH5a94Cv77gCd1A6voGyC-LsioioDnsI-hxn2foMBtM4Zfvwq13-B-OC7mVROrAvDTF-nPn_Es1CGPh9T4zaaqAc9gG6vUhHiLrkq_SXB3rl30ORouBmMy_XifDF6nJOeWNUTQ5dJwqqzQoCT4ZaFz0MIz5nWemfYPTS0I5o0pmfWSltwoobJSKc6ENqKLHk93dzH87CE1bh32sc2bHNdcU6mUka2KnlR5DClFKN0uVlsfD45Rd0TpWpTuiNKdUbaWh5OlAoB_ubVUSGrFH3XubTY</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Qian, Fangsheng</creator><creator>Chen, Ruo-Si</creator><creator>Wang, Ruopeng</creator><creator>Wang, Junjie</creator><creator>Xie, Peng</creator><creator>Mao, Jing-Yu</creator><creator>Lv, Ziyu</creator><creator>Ye, Shenghao</creator><creator>Yang, Jia-Qin</creator><creator>Wang, Zhanpeng</creator><creator>Zhou, Ye</creator><creator>Han, Su-Ting</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-3392-7569</orcidid></search><sort><creationdate>20221101</creationdate><title>A Leaky Integrate-and-Fire Neuron Based on Hexagonal Boron Nitride (h-BN) Monocrystalline Memristor</title><author>Qian, Fangsheng ; Chen, Ruo-Si ; Wang, Ruopeng ; Wang, Junjie ; Xie, Peng ; Mao, Jing-Yu ; Lv, Ziyu ; Ye, Shenghao ; Yang, Jia-Qin ; Wang, Zhanpeng ; Zhou, Ye ; Han, Su-Ting</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-30bb8205937e54eabd7ce73a11a7c68646709e31a88f19a40f285356f55213783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Boron</topic><topic>Boron nitride</topic><topic>Digital imaging</topic><topic>Electric fields</topic><topic>Electrodes</topic><topic>Hexagonal boron nitride (h-BN)</topic><topic>Interfacial energy</topic><topic>Ion migration</topic><topic>Memristors</topic><topic>monocrystalline</topic><topic>Monte Carlo simulation</topic><topic>Neurons</topic><topic>Performance evaluation</topic><topic>spiking neural network (SNN)</topic><topic>Switches</topic><topic>Switching</topic><topic>Threshold voltage</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qian, Fangsheng</creatorcontrib><creatorcontrib>Chen, Ruo-Si</creatorcontrib><creatorcontrib>Wang, Ruopeng</creatorcontrib><creatorcontrib>Wang, Junjie</creatorcontrib><creatorcontrib>Xie, Peng</creatorcontrib><creatorcontrib>Mao, Jing-Yu</creatorcontrib><creatorcontrib>Lv, Ziyu</creatorcontrib><creatorcontrib>Ye, Shenghao</creatorcontrib><creatorcontrib>Yang, Jia-Qin</creatorcontrib><creatorcontrib>Wang, Zhanpeng</creatorcontrib><creatorcontrib>Zhou, Ye</creatorcontrib><creatorcontrib>Han, Su-Ting</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on electron devices</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Qian, Fangsheng</au><au>Chen, Ruo-Si</au><au>Wang, Ruopeng</au><au>Wang, Junjie</au><au>Xie, Peng</au><au>Mao, Jing-Yu</au><au>Lv, Ziyu</au><au>Ye, Shenghao</au><au>Yang, Jia-Qin</au><au>Wang, Zhanpeng</au><au>Zhou, Ye</au><au>Han, Su-Ting</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Leaky Integrate-and-Fire Neuron Based on Hexagonal Boron Nitride (h-BN) Monocrystalline Memristor</atitle><jtitle>IEEE transactions on electron devices</jtitle><stitle>TED</stitle><date>2022-11-01</date><risdate>2022</risdate><volume>69</volume><issue>11</issue><spage>6049</spage><epage>6056</epage><pages>6049-6056</pages><issn>0018-9383</issn><eissn>1557-9646</eissn><coden>IETDAI</coden><abstract><![CDATA[As a competitive candidate for artificial neurons, memristors have become the focus of intense research owing to their intrinsic ion migration tunability, enabling an authentic implementation of biomimicry. However, they still suffer from variability issues due to 3-D uncontrollable filament dynamics in an amorphous medium and modeling of switching dynamics underlying filament growth and rupture is still under investigation. In this work, we present volatile memristors that exhibit desired characteristics for neuromorphic computing with low performance variations utilizing a hexagonal boron nitride (h-BN) monocrystalline as a switching medium. Theoretical investigations assisted by the Monte Carlo simulation combined with experimentally detected <inline-formula> <tex-math notation="LaTeX">{I} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">{V} </tex-math></inline-formula> characteristics described that the electric field dominates the set process, whereas the Gibbs-Thomson interfacial energy minimization and heat dissipation influence the relaxation process mostly. Additionally, h-BN memristors with high switching uniformity provide an ideal hardware platform for credible neuron emulation and software identification of digital images.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TED.2022.3206170</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-3392-7569</orcidid></addata></record> |
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| subjects | Boron Boron nitride Digital imaging Electric fields Electrodes Hexagonal boron nitride (h-BN) Interfacial energy Ion migration Memristors monocrystalline Monte Carlo simulation Neurons Performance evaluation spiking neural network (SNN) Switches Switching Threshold voltage |
| title | A Leaky Integrate-and-Fire Neuron Based on Hexagonal Boron Nitride (h-BN) Monocrystalline Memristor |
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