Y2O3-Based Crossbar Array for Analog and Neuromorphic Computation
Here, we report an implementation of ( 8\times8 ) \text{Y}_{{2}}\text{O}_{{3}} -based memristive crossbar array (MCA) out of a total dimension of ( 30\times25 ) array fabricated by utilizing a dual ion beam sputtering (DIBS) system. The selected ( 8\times8 ) MCA is further used to electrically writ...
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| Veröffentlicht in: | IEEE transactions on electron devices 2023-02, Vol.70 (2), p.473-477 |
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| container_title | IEEE transactions on electron devices |
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| creator | Kumar, Sanjay Kumbhar, Dhananjay D. Park, Jun H. Kamat, Rajanish K. Dongale, Tukaram D. Mukherjee, Shaibal |
| description | Here, we report an implementation of ( 8\times8 ) \text{Y}_{{2}}\text{O}_{{3}} -based memristive crossbar array (MCA) out of a total dimension of ( 30\times25 ) array fabricated by utilizing a dual ion beam sputtering (DIBS) system. The selected ( 8\times8 ) MCA is further used to electrically write random alphabets and perform synaptic learning characteristics to perform analog and neuromorphic computing applications. The MCA effectively exhibits multiple current levels and mimics various artificial synaptic properties with superior bidirectional switching responses. The MCA mimics potentiation, depression, and different Hebbian learning-based spike-time-dependent plasticity rules, suggesting the importance of the \text{Y}_{{2}}\text{O}_{{3}} -based MCA for large-scale neuromorphic and analog computations. This work provides different insights into the design of an artificial synapse by utilizing \text{Y}_{{2}}\text{O}_{{3}} as a switching oxide in memristors. |
| doi_str_mv | 10.1109/TED.2022.3227890 |
| format | Article |
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The selected (<inline-formula> <tex-math notation="LaTeX">8\times8 </tex-math></inline-formula>) MCA is further used to electrically write random alphabets and perform synaptic learning characteristics to perform analog and neuromorphic computing applications. The MCA effectively exhibits multiple current levels and mimics various artificial synaptic properties with superior bidirectional switching responses. The MCA mimics potentiation, depression, and different Hebbian learning-based spike-time-dependent plasticity rules, suggesting the importance of the <inline-formula> <tex-math notation="LaTeX">\text{Y}_{{2}}\text{O}_{{3}} </tex-math></inline-formula>-based MCA for large-scale neuromorphic and analog computations. This work provides different insights into the design of an artificial synapse by utilizing <inline-formula> <tex-math notation="LaTeX">\text{Y}_{{2}}\text{O}_{{3}} </tex-math></inline-formula> as a switching oxide in memristors.]]></description><identifier>ISSN: 0018-9383</identifier><identifier>EISSN: 1557-9646</identifier><identifier>DOI: 10.1109/TED.2022.3227890</identifier><identifier>CODEN: IETDAI</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Arrays ; Artificial synapse ; crossbar ; Depression ; Ion beam sputtering ; Learning ; Memristors ; neuromorphic computation ; Neuromorphic computing ; Neuromorphics ; spike-time-dependent plasticity (STDP) ; Switches ; Switching ; Synapses ; Voltage ; Writing ; Yttrium oxide ; Y₂O</subject><ispartof>IEEE transactions on electron devices, 2023-02, Vol.70 (2), p.473-477</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-9879-7278 ; 0000-0002-5770-9665 ; 0000-0001-5138-1622 ; 0000-0003-2536-6132 ; 0000-0001-5382-2006</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9992225$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9992225$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kumar, Sanjay</creatorcontrib><creatorcontrib>Kumbhar, Dhananjay D.</creatorcontrib><creatorcontrib>Park, Jun H.</creatorcontrib><creatorcontrib>Kamat, Rajanish K.</creatorcontrib><creatorcontrib>Dongale, Tukaram D.</creatorcontrib><creatorcontrib>Mukherjee, Shaibal</creatorcontrib><title>Y2O3-Based Crossbar Array for Analog and Neuromorphic Computation</title><title>IEEE transactions on electron devices</title><addtitle>TED</addtitle><description><![CDATA[Here, we report an implementation of (<inline-formula> <tex-math notation="LaTeX">8\times8 </tex-math></inline-formula>) <inline-formula> <tex-math notation="LaTeX">\text{Y}_{{2}}\text{O}_{{3}} </tex-math></inline-formula>-based memristive crossbar array (MCA) out of a total dimension of (<inline-formula> <tex-math notation="LaTeX">30\times25 </tex-math></inline-formula>) array fabricated by utilizing a dual ion beam sputtering (DIBS) system. The selected (<inline-formula> <tex-math notation="LaTeX">8\times8 </tex-math></inline-formula>) MCA is further used to electrically write random alphabets and perform synaptic learning characteristics to perform analog and neuromorphic computing applications. The MCA effectively exhibits multiple current levels and mimics various artificial synaptic properties with superior bidirectional switching responses. The MCA mimics potentiation, depression, and different Hebbian learning-based spike-time-dependent plasticity rules, suggesting the importance of the <inline-formula> <tex-math notation="LaTeX">\text{Y}_{{2}}\text{O}_{{3}} </tex-math></inline-formula>-based MCA for large-scale neuromorphic and analog computations. This work provides different insights into the design of an artificial synapse by utilizing <inline-formula> <tex-math notation="LaTeX">\text{Y}_{{2}}\text{O}_{{3}} </tex-math></inline-formula> as a switching oxide in memristors.]]></description><subject>Arrays</subject><subject>Artificial synapse</subject><subject>crossbar</subject><subject>Depression</subject><subject>Ion beam sputtering</subject><subject>Learning</subject><subject>Memristors</subject><subject>neuromorphic computation</subject><subject>Neuromorphic computing</subject><subject>Neuromorphics</subject><subject>spike-time-dependent plasticity (STDP)</subject><subject>Switches</subject><subject>Switching</subject><subject>Synapses</subject><subject>Voltage</subject><subject>Writing</subject><subject>Yttrium oxide</subject><subject>Y₂O</subject><issn>0018-9383</issn><issn>1557-9646</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNotjjtPwzAUhS0EEqWwI7FEYk7xvddxcscSykOq6FIGpshJbEjV1sFOBv49kcp0Hvp0dIS4BbkAkPywXT0tUCIuCDEvWJ6JGWRZnrJW-lzMpIQiZSroUlzFuJuiVgpnYvmJG0ofTbRtUgYfY21CsgzB_CbOT-5o9v4rMcc2ebdj8Acf-u-uSUp_6MfBDJ0_XosLZ_bR3vzrXHw8r7bla7revLyVy3XaoaQhVZJIIaCpGycRrGWypJ11hWvAuamu2zYDMAB1oXPTaE3Q1Irb1nHBjubi_rTbB_8z2jhUOz-G6V-sMNdMDKB4ou5OVGetrfrQHUz4rZgZETP6A1f2VKk</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Kumar, Sanjay</creator><creator>Kumbhar, Dhananjay D.</creator><creator>Park, Jun H.</creator><creator>Kamat, Rajanish K.</creator><creator>Dongale, Tukaram D.</creator><creator>Mukherjee, Shaibal</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>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-9879-7278</orcidid><orcidid>https://orcid.org/0000-0002-5770-9665</orcidid><orcidid>https://orcid.org/0000-0001-5138-1622</orcidid><orcidid>https://orcid.org/0000-0003-2536-6132</orcidid><orcidid>https://orcid.org/0000-0001-5382-2006</orcidid></search><sort><creationdate>20230201</creationdate><title>Y2O3-Based Crossbar Array for Analog and Neuromorphic Computation</title><author>Kumar, Sanjay ; Kumbhar, Dhananjay D. ; Park, Jun H. ; Kamat, Rajanish K. ; Dongale, Tukaram D. ; Mukherjee, Shaibal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-40334212abcf021ee93e36fef8fc1ffabcbdd511a11b867ac6631cb49ddf989f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Arrays</topic><topic>Artificial synapse</topic><topic>crossbar</topic><topic>Depression</topic><topic>Ion beam sputtering</topic><topic>Learning</topic><topic>Memristors</topic><topic>neuromorphic computation</topic><topic>Neuromorphic computing</topic><topic>Neuromorphics</topic><topic>spike-time-dependent plasticity (STDP)</topic><topic>Switches</topic><topic>Switching</topic><topic>Synapses</topic><topic>Voltage</topic><topic>Writing</topic><topic>Yttrium oxide</topic><topic>Y₂O</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kumar, Sanjay</creatorcontrib><creatorcontrib>Kumbhar, Dhananjay D.</creatorcontrib><creatorcontrib>Park, Jun H.</creatorcontrib><creatorcontrib>Kamat, Rajanish K.</creatorcontrib><creatorcontrib>Dongale, Tukaram D.</creatorcontrib><creatorcontrib>Mukherjee, Shaibal</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>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>Kumar, Sanjay</au><au>Kumbhar, Dhananjay D.</au><au>Park, Jun H.</au><au>Kamat, Rajanish K.</au><au>Dongale, Tukaram D.</au><au>Mukherjee, Shaibal</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Y2O3-Based Crossbar Array for Analog and Neuromorphic Computation</atitle><jtitle>IEEE transactions on electron devices</jtitle><stitle>TED</stitle><date>2023-02-01</date><risdate>2023</risdate><volume>70</volume><issue>2</issue><spage>473</spage><epage>477</epage><pages>473-477</pages><issn>0018-9383</issn><eissn>1557-9646</eissn><coden>IETDAI</coden><abstract><![CDATA[Here, we report an implementation of (<inline-formula> <tex-math notation="LaTeX">8\times8 </tex-math></inline-formula>) <inline-formula> <tex-math notation="LaTeX">\text{Y}_{{2}}\text{O}_{{3}} </tex-math></inline-formula>-based memristive crossbar array (MCA) out of a total dimension of (<inline-formula> <tex-math notation="LaTeX">30\times25 </tex-math></inline-formula>) array fabricated by utilizing a dual ion beam sputtering (DIBS) system. The selected (<inline-formula> <tex-math notation="LaTeX">8\times8 </tex-math></inline-formula>) MCA is further used to electrically write random alphabets and perform synaptic learning characteristics to perform analog and neuromorphic computing applications. The MCA effectively exhibits multiple current levels and mimics various artificial synaptic properties with superior bidirectional switching responses. The MCA mimics potentiation, depression, and different Hebbian learning-based spike-time-dependent plasticity rules, suggesting the importance of the <inline-formula> <tex-math notation="LaTeX">\text{Y}_{{2}}\text{O}_{{3}} </tex-math></inline-formula>-based MCA for large-scale neuromorphic and analog computations. This work provides different insights into the design of an artificial synapse by utilizing <inline-formula> <tex-math notation="LaTeX">\text{Y}_{{2}}\text{O}_{{3}} </tex-math></inline-formula> as a switching oxide in memristors.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TED.2022.3227890</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-9879-7278</orcidid><orcidid>https://orcid.org/0000-0002-5770-9665</orcidid><orcidid>https://orcid.org/0000-0001-5138-1622</orcidid><orcidid>https://orcid.org/0000-0003-2536-6132</orcidid><orcidid>https://orcid.org/0000-0001-5382-2006</orcidid></addata></record> |
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| subjects | Arrays Artificial synapse crossbar Depression Ion beam sputtering Learning Memristors neuromorphic computation Neuromorphic computing Neuromorphics spike-time-dependent plasticity (STDP) Switches Switching Synapses Voltage Writing Yttrium oxide Y₂O |
| title | Y2O3-Based Crossbar Array for Analog and Neuromorphic Computation |
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