Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics
The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain‐inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs...
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description | The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain‐inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low‐dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low‐power‐switching capability, and hetero‐integration compatibility. Hence, a large number of experimental demonstrations on 2D material‐based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk‐material‐based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material‐based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D‐memristor‐based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.
The recent progress regarding memristors based on 2D materials for neuromorphic electronics is reviewed. They are categorized by their various materials, structures, and mechanisms. Owing to their outstanding synaptic characteristics, neuromorphic devices based on 2D materials offer excellent performance, energy‐efficient operation, and modifiable synaptic functions applicable to complex learning. |
doi_str_mv | 10.1002/adma.202002092 |
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The recent progress regarding memristors based on 2D materials for neuromorphic electronics is reviewed. They are categorized by their various materials, structures, and mechanisms. Owing to their outstanding synaptic characteristics, neuromorphic devices based on 2D materials offer excellent performance, energy‐efficient operation, and modifiable synaptic functions applicable to complex learning.</description><identifier>ISSN: 0935-9648</identifier><identifier>ISSN: 1521-4095</identifier><identifier>EISSN: 1521-4095</identifier><identifier>DOI: 10.1002/adma.202002092</identifier><identifier>PMID: 32985042</identifier><language>eng</language><publisher>Germany: Wiley Subscription Services, Inc</publisher><subject>2D materials ; artificial synapses ; Biomimetic Materials - chemistry ; Biomimetics - instrumentation ; Biomimetics - methods ; Brain ; Electronic components ; Electronics ; Humans ; Layered materials ; Materials science ; Memristors ; Metal oxides ; Nanomaterials ; Nanostructures - chemistry ; Neural networks ; Neural Networks, Computer ; neuromorphic electronics ; Organic materials ; Oxides - chemistry ; Physical properties ; Stability ; Synapses ; Synapses - physiology ; transition metal dichalcogenides ; Two dimensional materials</subject><ispartof>Advanced materials (Weinheim), 2020-12, Vol.32 (51), p.e2002092-n/a</ispartof><rights>2020 Wiley‐VCH GmbH</rights><rights>2020 Wiley-VCH GmbH.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4392-f4c03cfdbdab3c254201193798abf789ef8ea6008aafab3ee16935ec6c87a9bd3</citedby><cites>FETCH-LOGICAL-c4392-f4c03cfdbdab3c254201193798abf789ef8ea6008aafab3ee16935ec6c87a9bd3</cites><orcidid>0000-0003-1570-8688</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fadma.202002092$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fadma.202002092$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32985042$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Huh, Woong</creatorcontrib><creatorcontrib>Lee, Donghun</creatorcontrib><creatorcontrib>Lee, Chul‐Ho</creatorcontrib><title>Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics</title><title>Advanced materials (Weinheim)</title><addtitle>Adv Mater</addtitle><description>The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain‐inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low‐dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low‐power‐switching capability, and hetero‐integration compatibility. Hence, a large number of experimental demonstrations on 2D material‐based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk‐material‐based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material‐based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D‐memristor‐based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.
The recent progress regarding memristors based on 2D materials for neuromorphic electronics is reviewed. They are categorized by their various materials, structures, and mechanisms. Owing to their outstanding synaptic characteristics, neuromorphic devices based on 2D materials offer excellent performance, energy‐efficient operation, and modifiable synaptic functions applicable to complex learning.</description><subject>2D materials</subject><subject>artificial synapses</subject><subject>Biomimetic Materials - chemistry</subject><subject>Biomimetics - instrumentation</subject><subject>Biomimetics - methods</subject><subject>Brain</subject><subject>Electronic components</subject><subject>Electronics</subject><subject>Humans</subject><subject>Layered materials</subject><subject>Materials science</subject><subject>Memristors</subject><subject>Metal oxides</subject><subject>Nanomaterials</subject><subject>Nanostructures - chemistry</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>neuromorphic electronics</subject><subject>Organic materials</subject><subject>Oxides - chemistry</subject><subject>Physical properties</subject><subject>Stability</subject><subject>Synapses</subject><subject>Synapses - physiology</subject><subject>transition metal dichalcogenides</subject><subject>Two dimensional materials</subject><issn>0935-9648</issn><issn>1521-4095</issn><issn>1521-4095</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqF0MlLxDAUBvAgio7L1aMEvHjp-JJuyXEcV3D04HItafqCkbYZkxaZ_97IuIAXIZAQfu8j-Qg5ZDBlAPxUNZ2acuDxDJJvkAnLOUsykPkmmYBM80QWmdghuyG8AoAsoNgmOymXIoeMT8jzAjtvw-B8oGcqYENdT_k5XagBvVVtoCquns78YI3V8YY-rHq1DEiN8_QOR-8655cvVtOLFvXgXW912CdbJg7jwde-R54uLx7n18nt_dXNfHab6CyVPDGZhlSbpm5UnWqeZxwYk2kphapNKSQagaoAEEqZKBBZEb-EutCiVLJu0j1yss5devc2YhiqzgaNbat6dGOoeJYVUrJUiEiP_9BXN_o-vi6qkpWQ5wWLarpW2rsQPJpq6W2n_KpiUH02Xn02Xv00HgeOvmLHusPmh39XHIFcg3fb4uqfuGp2vpj9hn8AjqiMrA</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Huh, Woong</creator><creator>Lee, Donghun</creator><creator>Lee, Chul‐Ho</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1570-8688</orcidid></search><sort><creationdate>20201201</creationdate><title>Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics</title><author>Huh, Woong ; Lee, Donghun ; Lee, Chul‐Ho</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4392-f4c03cfdbdab3c254201193798abf789ef8ea6008aafab3ee16935ec6c87a9bd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>2D materials</topic><topic>artificial synapses</topic><topic>Biomimetic Materials - chemistry</topic><topic>Biomimetics - instrumentation</topic><topic>Biomimetics - methods</topic><topic>Brain</topic><topic>Electronic components</topic><topic>Electronics</topic><topic>Humans</topic><topic>Layered materials</topic><topic>Materials science</topic><topic>Memristors</topic><topic>Metal oxides</topic><topic>Nanomaterials</topic><topic>Nanostructures - chemistry</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>neuromorphic electronics</topic><topic>Organic materials</topic><topic>Oxides - chemistry</topic><topic>Physical properties</topic><topic>Stability</topic><topic>Synapses</topic><topic>Synapses - physiology</topic><topic>transition metal dichalcogenides</topic><topic>Two dimensional materials</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huh, Woong</creatorcontrib><creatorcontrib>Lee, Donghun</creatorcontrib><creatorcontrib>Lee, Chul‐Ho</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>MEDLINE - Academic</collection><jtitle>Advanced materials (Weinheim)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huh, Woong</au><au>Lee, Donghun</au><au>Lee, Chul‐Ho</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics</atitle><jtitle>Advanced materials (Weinheim)</jtitle><addtitle>Adv Mater</addtitle><date>2020-12-01</date><risdate>2020</risdate><volume>32</volume><issue>51</issue><spage>e2002092</spage><epage>n/a</epage><pages>e2002092-n/a</pages><issn>0935-9648</issn><issn>1521-4095</issn><eissn>1521-4095</eissn><abstract>The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain‐inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low‐dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low‐power‐switching capability, and hetero‐integration compatibility. Hence, a large number of experimental demonstrations on 2D material‐based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk‐material‐based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material‐based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D‐memristor‐based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.
The recent progress regarding memristors based on 2D materials for neuromorphic electronics is reviewed. They are categorized by their various materials, structures, and mechanisms. Owing to their outstanding synaptic characteristics, neuromorphic devices based on 2D materials offer excellent performance, energy‐efficient operation, and modifiable synaptic functions applicable to complex learning.</abstract><cop>Germany</cop><pub>Wiley Subscription Services, Inc</pub><pmid>32985042</pmid><doi>10.1002/adma.202002092</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-1570-8688</orcidid></addata></record> |
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subjects | 2D materials artificial synapses Biomimetic Materials - chemistry Biomimetics - instrumentation Biomimetics - methods Brain Electronic components Electronics Humans Layered materials Materials science Memristors Metal oxides Nanomaterials Nanostructures - chemistry Neural networks Neural Networks, Computer neuromorphic electronics Organic materials Oxides - chemistry Physical properties Stability Synapses Synapses - physiology transition metal dichalcogenides Two dimensional materials |
title | Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics |
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