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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Weinheim) 2020-12, Vol.32 (51), p.e2002092-n/a
Hauptverfasser: Huh, Woong, Lee, Donghun, Lee, Chul‐Ho
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page n/a
container_issue 51
container_start_page e2002092
container_title Advanced materials (Weinheim)
container_volume 32
creator Huh, Woong
Lee, Donghun
Lee, Chul‐Ho
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
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2446991388</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2446991388</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4392-f4c03cfdbdab3c254201193798abf789ef8ea6008aafab3ee16935ec6c87a9bd3</originalsourceid><addsrcrecordid>eNqF0MlLxDAUBvAgio7L1aMEvHjp-JJuyXEcV3D04HItafqCkbYZkxaZ_97IuIAXIZAQfu8j-Qg5ZDBlAPxUNZ2acuDxDJJvkAnLOUsykPkmmYBM80QWmdghuyG8AoAsoNgmOymXIoeMT8jzAjtvw-B8oGcqYENdT_k5XagBvVVtoCquns78YI3V8YY-rHq1DEiN8_QOR-8655cvVtOLFvXgXW912CdbJg7jwde-R54uLx7n18nt_dXNfHab6CyVPDGZhlSbpm5UnWqeZxwYk2kphapNKSQagaoAEEqZKBBZEb-EutCiVLJu0j1yss5devc2YhiqzgaNbat6dGOoeJYVUrJUiEiP_9BXN_o-vi6qkpWQ5wWLarpW2rsQPJpq6W2n_KpiUH02Xn02Xv00HgeOvmLHusPmh39XHIFcg3fb4uqfuGp2vpj9hn8AjqiMrA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2471705561</pqid></control><display><type>article</type><title>Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics</title><source>Wiley Online Library - AutoHoldings Journals</source><source>MEDLINE</source><creator>Huh, Woong ; Lee, Donghun ; Lee, Chul‐Ho</creator><creatorcontrib>Huh, Woong ; Lee, Donghun ; Lee, Chul‐Ho</creatorcontrib><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><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>
fulltext fulltext
identifier ISSN: 0935-9648
ispartof Advanced materials (Weinheim), 2020-12, Vol.32 (51), p.e2002092-n/a
issn 0935-9648
1521-4095
1521-4095
language eng
recordid cdi_proquest_miscellaneous_2446991388
source Wiley Online Library - AutoHoldings Journals; MEDLINE
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
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T19%3A16%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Memristors%20Based%20on%202D%20Materials%20as%20an%20Artificial%20Synapse%20for%20Neuromorphic%20Electronics&rft.jtitle=Advanced%20materials%20(Weinheim)&rft.au=Huh,%20Woong&rft.date=2020-12-01&rft.volume=32&rft.issue=51&rft.spage=e2002092&rft.epage=n/a&rft.pages=e2002092-n/a&rft.issn=0935-9648&rft.eissn=1521-4095&rft_id=info:doi/10.1002/adma.202002092&rft_dat=%3Cproquest_cross%3E2446991388%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2471705561&rft_id=info:pmid/32985042&rfr_iscdi=true