Tilt‐Engineered Molecular‐Scale Selector for Enhanced Learning in Artificial Neural Networks
Miniaturization of individual selectors in crossbar‐array‐based artificial neural networks is essential for the advancement of the underlying neuromorphic electronics, as it improves learning, recognition, and prediction accuracies. This study proposes a tilt‐engineered molecular‐scale selector comp...
Uloženo v:
| Vydáno v: | Advanced functional materials Ročník 34; číslo 16 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Hoboken
Wiley Subscription Services, Inc
01.04.2024
|
| Témata: | |
| ISSN: | 1616-301X, 1616-3028 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Miniaturization of individual selectors in crossbar‐array‐based artificial neural networks is essential for the advancement of the underlying neuromorphic electronics, as it improves learning, recognition, and prediction accuracies. This study proposes a tilt‐engineered molecular‐scale selector comprising a heterostructure of biphenyl‐4‐thiol (OPT2) or 1‐octanethiol (C8) molecular layers and an n‐type two‐dimensional MoS2 monolayer (1L‐MoS2) at an approximate contact radius of 3 nm, which is evaluated via conductive atomic force microscopy under various tip‐loading forces. The molecular tilt configuration controlled by the tip‐loading force is used as a rectifying engineer for the OPT2/1L‐MoS2 and C8/1L‐MoS2 heterojunction accuracies. Rectification ratios and conductance levels are significantly influenced by the molecular backbones and tilt angle. The proposed tilt‐engineered selector can aid in controlling undesired neural signals affecting vector–matrix multiplications and adjusting the switching range compatibility of an integrated synaptic device cell, significantly influencing the pattern recognition accuracy. By controlling the tilt angle, the recognition accuracy on the MNIST dataset increases from 78.65% to 86.45% and from 7.74% to 86.09% when using the OPT2/1L‐MoS2 and C8/1L‐MoS2 selector, respectively. The proposed molecular tilt configuration can be used for developing customized molecular‐scale selectors for crossbar‐array‐based artificial neural networks to improve learning while suppressing undesired neural signals.
Tailored molecular‐scale selectors engineered by molecular tilt configuration in crossbar array‐based artificial neural networks is presented at the ultimate scale limit (at a contact radius of ≈3 nm). This tilt‐engineering molecular‐scale selector can aid in mitigating undesired neural signals with adjustment of the switching range compatibility of an integrated synaptic device cell which significantly influenced the pattern recognition accuracy. |
|---|---|
| AbstractList | Miniaturization of individual selectors in crossbar‐array‐based artificial neural networks is essential for the advancement of the underlying neuromorphic electronics, as it improves learning, recognition, and prediction accuracies. This study proposes a tilt‐engineered molecular‐scale selector comprising a heterostructure of biphenyl‐4‐thiol (OPT2) or 1‐octanethiol (C8) molecular layers and an n‐type two‐dimensional MoS2 monolayer (1L‐MoS2) at an approximate contact radius of 3 nm, which is evaluated via conductive atomic force microscopy under various tip‐loading forces. The molecular tilt configuration controlled by the tip‐loading force is used as a rectifying engineer for the OPT2/1L‐MoS2 and C8/1L‐MoS2 heterojunction accuracies. Rectification ratios and conductance levels are significantly influenced by the molecular backbones and tilt angle. The proposed tilt‐engineered selector can aid in controlling undesired neural signals affecting vector–matrix multiplications and adjusting the switching range compatibility of an integrated synaptic device cell, significantly influencing the pattern recognition accuracy. By controlling the tilt angle, the recognition accuracy on the MNIST dataset increases from 78.65% to 86.45% and from 7.74% to 86.09% when using the OPT2/1L‐MoS2 and C8/1L‐MoS2 selector, respectively. The proposed molecular tilt configuration can be used for developing customized molecular‐scale selectors for crossbar‐array‐based artificial neural networks to improve learning while suppressing undesired neural signals.
Tailored molecular‐scale selectors engineered by molecular tilt configuration in crossbar array‐based artificial neural networks is presented at the ultimate scale limit (at a contact radius of ≈3 nm). This tilt‐engineering molecular‐scale selector can aid in mitigating undesired neural signals with adjustment of the switching range compatibility of an integrated synaptic device cell which significantly influenced the pattern recognition accuracy. Miniaturization of individual selectors in crossbar‐array‐based artificial neural networks is essential for the advancement of the underlying neuromorphic electronics, as it improves learning, recognition, and prediction accuracies. This study proposes a tilt‐engineered molecular‐scale selector comprising a heterostructure of biphenyl‐4‐thiol (OPT2) or 1‐octanethiol (C8) molecular layers and an n‐type two‐dimensional MoS 2 monolayer (1 L ‐MoS 2 ) at an approximate contact radius of 3 nm, which is evaluated via conductive atomic force microscopy under various tip‐loading forces. The molecular tilt configuration controlled by the tip‐loading force is used as a rectifying engineer for the OPT2/1 L ‐MoS 2 and C8/1 L ‐MoS 2 heterojunction accuracies. Rectification ratios and conductance levels are significantly influenced by the molecular backbones and tilt angle. The proposed tilt‐engineered selector can aid in controlling undesired neural signals affecting vector–matrix multiplications and adjusting the switching range compatibility of an integrated synaptic device cell, significantly influencing the pattern recognition accuracy. By controlling the tilt angle, the recognition accuracy on the MNIST dataset increases from 78.65% to 86.45% and from 7.74% to 86.09% when using the OPT2/1 L ‐MoS 2 and C8/1 L ‐MoS 2 selector, respectively. The proposed molecular tilt configuration can be used for developing customized molecular‐scale selectors for crossbar‐array‐based artificial neural networks to improve learning while suppressing undesired neural signals. Miniaturization of individual selectors in crossbar‐array‐based artificial neural networks is essential for the advancement of the underlying neuromorphic electronics, as it improves learning, recognition, and prediction accuracies. This study proposes a tilt‐engineered molecular‐scale selector comprising a heterostructure of biphenyl‐4‐thiol (OPT2) or 1‐octanethiol (C8) molecular layers and an n‐type two‐dimensional MoS2 monolayer (1L‐MoS2) at an approximate contact radius of 3 nm, which is evaluated via conductive atomic force microscopy under various tip‐loading forces. The molecular tilt configuration controlled by the tip‐loading force is used as a rectifying engineer for the OPT2/1L‐MoS2 and C8/1L‐MoS2 heterojunction accuracies. Rectification ratios and conductance levels are significantly influenced by the molecular backbones and tilt angle. The proposed tilt‐engineered selector can aid in controlling undesired neural signals affecting vector–matrix multiplications and adjusting the switching range compatibility of an integrated synaptic device cell, significantly influencing the pattern recognition accuracy. By controlling the tilt angle, the recognition accuracy on the MNIST dataset increases from 78.65% to 86.45% and from 7.74% to 86.09% when using the OPT2/1L‐MoS2 and C8/1L‐MoS2 selector, respectively. The proposed molecular tilt configuration can be used for developing customized molecular‐scale selectors for crossbar‐array‐based artificial neural networks to improve learning while suppressing undesired neural signals. |
| Author | Jeon, Takgyeong Shin, Jaeho Eo, Jung Sun Wang, Gunuk Jang, Jingon |
| Author_xml | – sequence: 1 givenname: Jung Sun orcidid: 0000-0003-2086-2882 surname: Eo fullname: Eo, Jung Sun organization: Korea University – sequence: 2 givenname: Jaeho orcidid: 0000-0002-7739-7195 surname: Shin fullname: Shin, Jaeho organization: Rice University – sequence: 3 givenname: Takgyeong orcidid: 0000-0002-8878-6305 surname: Jeon fullname: Jeon, Takgyeong organization: Korea University – sequence: 4 givenname: Jingon orcidid: 0000-0002-1992-177X surname: Jang fullname: Jang, Jingon email: jangjg@korea.ac.kr organization: Korea University – sequence: 5 givenname: Gunuk orcidid: 0000-0001-6059-0530 surname: Wang fullname: Wang, Gunuk email: gunukwang@korea.ac.kr organization: Korea Institute of Science and Technology |
| BookMark | eNqFkM1OAjEUhRuDiYBuXU_ievD2h5ZZEgQ1GXQBJu5qZ2ixOHSwMxPCzkfwGX0Sixhcurg5NyffuTc5HdRypdMIXWLoYQByrRZm3SNAKMYY6AlqY455TIEMWscdP5-hTlWtALAQlLXRy9wW9dfH59gtrdPa60U0LQudN4XywZ7lqtDRTAenLn1kwozdq3J54FKtvLNuGVkXDX1tjc2tKqIH3fgfqbelf6vO0alRRaUvfrWLnibj-eguTh9v70fDNM6JIDReZJxlgmR9nJFMGANmwBJQjGZEJGA415hwCD4HThnHoIwxfUYTlYgcFKFddHW4u_Hle6OrWq7KxrvwUlJggAeCsSRQvQOV-7KqvDZy4-1a-Z3EIPctyn2L8thiCCSHwNYWevcPLYc3k-lf9hvOBnmL |
| Cites_doi | 10.1109/TNANO.2002.1005426 10.1021/acsami.9b08166 10.1038/nmat4703 10.1038/nnano.2017.110 10.1016/j.colsurfa.2005.11.052 10.1021/ja202178k 10.1038/s41565-018-0302-0 10.1038/s41928-020-00473-w 10.1016/j.mejo.2012.10.001 10.1109/5.752515 10.1021/acsnano.1c00002 10.1002/advs.202101390 10.1038/nmat3778 10.1021/jp3041204 10.1126/science.278.5336.252 10.1002/adma.202002092 10.1021/ja207751w 10.1109/MSP.2012.2211477 10.1109/TED.2012.2193129 10.1021/acs.jpcc.2c09086 10.1002/smtd.202200646 10.1021/acs.nanolett.2c00922 10.1038/s41427-018-0101-y 10.1038/s41377-019-0151-0 10.1038/nnano.2012.238 10.1038/nature14441 10.1002/adfm.201600680 10.1007/s40747-021-00282-4 10.1126/science.1137149 10.1109/TED.2012.2225147 10.3390/ma13225089 10.1007/s00339-011-6294-3 10.1038/ncomms7324 10.1002/adfm.201303520 10.1016/j.mtnano.2023.100319 10.1002/adfm.201808376 10.1021/acsnano.8b00538 10.1109/TED.2017.2776085 10.1038/nnano.2014.150 10.1038/s41467-020-15144-9 10.1021/ja900773h 10.1016/0169-7439(93)80052-J 10.1002/aisy.202000149 10.1021/nl9021094 10.1038/s42256-019-0089-1 10.1002/aelm.202100558 10.1038/s41467-018-04484-2 10.1109/LED.2011.2163697 10.1002/adma.201102395 10.1016/j.orgel.2011.08.017 10.1016/j.rinp.2018.12.092 10.1021/acsnano.6b07159 10.1002/aelm.201600326 10.1007/s40820-023-01035-3 10.1088/0957-4484/21/7/075501 10.1021/acs.nanolett.5b02190 10.1023/A:1018966222807 10.1002/adma.201302047 10.1038/nnano.2012.240 10.1021/jp204340w 10.1039/D0NA00100G 10.1021/acsami.2c11016 10.1002/adma.201405110 10.1021/cm5014784 10.1038/s41467-023-39033-z 10.1021/nl503897h 10.1021/jacs.8b13370 10.1002/admt.202200193 10.1021/acs.nanolett.8b01294 |
| ContentType | Journal Article |
| Copyright | 2023 Wiley‐VCH GmbH 2024 Wiley‐VCH GmbH |
| Copyright_xml | – notice: 2023 Wiley‐VCH GmbH – notice: 2024 Wiley‐VCH GmbH |
| DBID | AAYXX CITATION 7SP 7SR 7U5 8BQ 8FD JG9 L7M |
| DOI | 10.1002/adfm.202311103 |
| DatabaseName | CrossRef Electronics & Communications Abstracts Engineered Materials Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database Materials Research Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Electronics & Communications Abstracts Solid State and Superconductivity Abstracts Advanced Technologies Database with Aerospace METADEX |
| DatabaseTitleList | CrossRef Materials Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1616-3028 |
| EndPage | n/a |
| ExternalDocumentID | 10_1002_adfm_202311103 ADFM202311103 |
| Genre | article |
| GrantInformation_xml | – fundername: National R&D program administered by the National Research Foundation (NRF) of Korea and funded by Ministry of Science and ICT (MIST) of Korea government funderid: RS‐2023‐00220077; 2022M3H4A1A01009526; 2022R1A2B5B02001455; 2022M3H4A1A01009656; RS‐2023‐00245664 – fundername: Korea University Research Grant – fundername: Korea University‐Korea Institute of Science and Technology (KU‐KIST) Graduate School Program of Korea University – fundername: Korea Institute of Science and Technology (KIST) Institutional Program funderid: 2E32491‐23‐112 |
| GroupedDBID | -~X .3N .GA 05W 0R~ 10A 1L6 1OC 23M 33P 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5VS 66C 6P2 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AAHQN AAMNL AANLZ AAONW AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABIJN ABJNI ABPVW ACAHQ ACCFJ ACCZN ACGFS ACIWK ACPOU ACSCC ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN ADZOD AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ATUGU AUFTA AZBYB AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM EBS F00 F01 F04 F5P G-S G.N GNP GODZA H.T H.X HBH HGLYW HHY HHZ HZ~ IX1 J0M JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG P2P P2W P2X P4D Q.N Q11 QB0 QRW R.K RNS ROL RWI RX1 RYL SUPJJ UB1 V2E W8V W99 WBKPD WFSAM WIH WIK WJL WOHZO WQJ WRC WXSBR WYISQ XG1 XPP XV2 ~IA ~WT .Y3 31~ AAMMB AANHP AASGY AAYXX ACBWZ ACRPL ACYXJ ADMLS ADNMO AEFGJ AEYWJ AGHNM AGQPQ AGXDD AGYGG AIDQK AIDYY ASPBG AVWKF AZFZN CITATION EJD FEDTE HF~ HVGLF LW6 O8X 7SP 7SR 7U5 8BQ 8FD JG9 L7M |
| ID | FETCH-LOGICAL-c2723-db64b72b51b2b7ff0f8490a43b2790f66e1260ff060634610afff5439a97c0a23 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001133196600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1616-301X |
| IngestDate | Sun Jul 13 04:57:48 EDT 2025 Sat Nov 29 07:24:48 EST 2025 Wed Jan 22 17:21:17 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 16 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2723-db64b72b51b2b7ff0f8490a43b2790f66e1260ff060634610afff5439a97c0a23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-8878-6305 0000-0003-2086-2882 0000-0002-7739-7195 0000-0002-1992-177X 0000-0001-6059-0530 |
| PQID | 3040187449 |
| PQPubID | 2045204 |
| PageCount | 11 |
| ParticipantIDs | proquest_journals_3040187449 crossref_primary_10_1002_adfm_202311103 wiley_primary_10_1002_adfm_202311103_ADFM202311103 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-04-01 |
| PublicationDateYYYYMMDD | 2024-04-01 |
| PublicationDate_xml | – month: 04 year: 2024 text: 2024-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken |
| PublicationPlace_xml | – name: Hoboken |
| PublicationTitle | Advanced functional materials |
| PublicationYear | 2024 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | 1997; 278 2012; 60 2011; 115 2013; 25 2019; 11 2019; 12 2019; 14 2014; 26 1999; 87 2014; 24 2020; 13 2011; 12 2020; 11 2012; 59 2022; 22 2013; 8 2018; 9 2010; 21 2020; 3 2023; 22 2020; 2 2006; 284 1985 2019; 29 2014; 13 2012; 29 2011; 23 2014; 9 2019; 8 2021; 8 2021; 7 2015; 15 2015; 6 2023; 14 2013; 44 2011 2023; 15 2015; 521 2019; 1 2017; 65 2002; 1 2023; 127 2011; 32 2020; 32 2009; 131 2019; 141 2011; 133 2018; 18 2021; 15 2015; 27 1993; 18 2016; 2 2007; 315 2023 2022 2021 2017; 16 2022; 6 2022; 7 2017; 11 2022; 8 1993; 10 2017; 12 2018 2022; 14 2009; 9 2013 2018; 12 2018; 10 2012; 116 2016; 26 e_1_2_8_28_1 e_1_2_8_24_1 e_1_2_8_47_1 Cassuto Y. (e_1_2_8_20_1) 2013 e_1_2_8_26_1 e_1_2_8_49_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_43_1 e_1_2_8_66_1 Li Y. (e_1_2_8_25_1) 2013 e_1_2_8_22_1 e_1_2_8_45_1 e_1_2_8_64_1 e_1_2_8_62_1 e_1_2_8_1_1 e_1_2_8_41_1 e_1_2_8_60_1 e_1_2_8_17_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_59_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_57_1 e_1_2_8_70_1 e_1_2_8_32_1 e_1_2_8_55_1 e_1_2_8_78_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_76_1 e_1_2_8_51_1 e_1_2_8_74_1 e_1_2_8_30_1 e_1_2_8_72_1 e_1_2_8_29_1 Lee K. (e_1_2_8_9_1) 2022 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_48_1 e_1_2_8_2_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_67_1 e_1_2_8_44_1 e_1_2_8_65_1 Kim T. H. (e_1_2_8_52_1) 2023 e_1_2_8_63_1 e_1_2_8_40_1 e_1_2_8_61_1 Cho H. (e_1_2_8_68_1) 2023 e_1_2_8_18_1 e_1_2_8_39_1 Khan M. U. (e_1_2_8_3_1) 2021 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_58_1 e_1_2_8_79_1 Huang J.‐J. (e_1_2_8_69_1) 2011 Park G. W. Y. (e_1_2_8_23_1) 2023 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_56_1 e_1_2_8_77_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_75_1 e_1_2_8_73_1 e_1_2_8_50_1 e_1_2_8_71_1 |
| References_xml | – year: 1985 – volume: 22 start-page: 4429 year: 2022 publication-title: Nano Lett. – volume: 87 start-page: 537 year: 1999 publication-title: Proc. IEEE – volume: 9 start-page: 676 year: 2014 publication-title: Nat. Nanotechonol. – volume: 3 start-page: 638 year: 2020 publication-title: Nat. Electon. – volume: 10 start-page: 1097 year: 2018 publication-title: NPG Asia Mater – volume: 6 year: 2022 publication-title: Small Methods – volume: 7 year: 2021 publication-title: Adv. Electron. Mater. – volume: 131 start-page: 5980 year: 2009 publication-title: J. Am. Chem. Soc. – volume: 22 year: 2023 publication-title: Mater. Today Nano – volume: 12 start-page: 797 year: 2017 publication-title: Nat. Nanotechonol. – volume: 15 start-page: 1031 year: 2015 publication-title: Nano Lett. – volume: 6 start-page: 6324 year: 2015 publication-title: Nat. Commun. – year: 2018 – volume: 1 start-page: 56 year: 2002 publication-title: IEEE Trans. Nanotechnol. – volume: 65 start-page: 122 year: 2017 publication-title: IEEE Trans. Electron Devices – volume: 27 start-page: 1426 year: 2015 publication-title: Adv. Mater. – volume: 284 start-page: 583 year: 2006 publication-title: Colloids Surf. A: Physicochem. Eng. Asp. – year: 2023 publication-title: Jpn. J. Appl. Phys. – volume: 116 year: 2012 publication-title: J. Phys. Chem. C – volume: 2 start-page: 1811 year: 2020 publication-title: Nanoscale Adv – volume: 29 year: 2019 publication-title: Adv. Funct. Mater. – start-page: 109 year: 2011 publication-title: J. Appl. Phys. – volume: 315 start-page: 1568 year: 2007 publication-title: Science – volume: 141 start-page: 3670 year: 2019 publication-title: J. Am. Chem. Soc. – volume: 2 year: 2020 publication-title: Adv. Intel. Syst. – volume: 2 year: 2016 publication-title: Adv. Electron. Mater. – volume: 15 start-page: 8484 year: 2021 publication-title: ACS Nano – volume: 21 year: 2010 publication-title: Nanotechnology – volume: 44 start-page: 176 year: 2013 publication-title: Microelectron. J. – volume: 8 start-page: 42 year: 2019 publication-title: Light Sci. Appl. – volume: 13 start-page: 50 year: 2014 publication-title: Nat. Mater. – volume: 15 start-page: 6009 year: 2015 publication-title: Nano Lett. – volume: 23 start-page: 4063 year: 2011 publication-title: Adv. Mater. – volume: 14 start-page: 35 year: 2019 publication-title: Nat. Nanotechonol. – start-page: 156 year: 2013 publication-title: IEEE Int. Sympo. Inf. Theory – volume: 11 start-page: 1412 year: 2020 publication-title: Nat. Commun. – volume: 59 start-page: 1813 year: 2012 publication-title: IEEE Trans. Electron Devices – volume: 8 year: 2021 publication-title: Adv. Sci. – start-page: 64 year: 2013 publication-title: IEEE/ACM Int. Symp. Nanoscale Arch. (NANOARCH) – volume: 7 year: 2022 publication-title: Adv. Mater. Technol. – volume: 1 start-page: 434 year: 2019 publication-title: Nat. Mach. Intell. – year: 2023 publication-title: Adv. Funct. Mater. – volume: 14 start-page: 3285 year: 2023 publication-title: Nat. Commun. – volume: 15 start-page: 69 year: 2023 publication-title: Nanomicro Lett – year: 2011 publication-title: Int. Electron Devices Meeting – volume: 12 start-page: 2144 year: 2011 publication-title: Org. Electron. – volume: 60 start-page: 420 year: 2012 publication-title: IEEE Trans. Electron Devices – volume: 127 start-page: 6025 year: 2023 publication-title: J. Phys. Chem. C – volume: 115 year: 2011 publication-title: J. Phys. Chem. C – volume: 13 start-page: 5089 year: 2020 publication-title: Materials – volume: 10 start-page: 165 year: 1993 publication-title: Pharm. Res. – volume: 26 start-page: 5290 year: 2016 publication-title: Adv. Funct. Mater. – volume: 32 start-page: 1579 year: 2011 publication-title: IEEE Electron Device Lett. – volume: 133 start-page: 8838 year: 2011 publication-title: J. Am. Chem. Soc. – volume: 12 start-page: 3551 year: 2018 publication-title: ACS Nano – volume: 8 start-page: 113 year: 2013 publication-title: Nat. Nanotechonol. – volume: 18 start-page: 4322 year: 2018 publication-title: Nano Lett. – volume: 9 start-page: 3909 year: 2009 publication-title: Nano Lett. – volume: 18 start-page: 115 year: 1993 publication-title: Chemom. Intell. Lab. Syst. – volume: 14 year: 2022 publication-title: ACS Appl. Mater. Int. – start-page: 6, 619 year: 2023 publication-title: Nat. Electron. – volume: 11 year: 2019 publication-title: ACS Appl. Mater. Interfaces – volume: 9 start-page: 2385 year: 2018 publication-title: Nat. Commun. – volume: 24 start-page: 5316 year: 2014 publication-title: Adv. Funct. Mater. – volume: 278 start-page: 252 year: 1997 publication-title: Science – volume: 521 start-page: 61 year: 2015 publication-title: Nature – volume: 8 start-page: 787 year: 2022 publication-title: Complex Intell. Syst. – volume: 32 year: 2020 publication-title: Adv. Mater. – volume: 25 start-page: 4789 year: 2013 publication-title: Adv. Mater. – volume: 133 year: 2011 publication-title: J. Am. Chem. Soc. – volume: 26 start-page: 3938 year: 2014 publication-title: Chem. Mater. – volume: 11 start-page: 1588 year: 2017 publication-title: ACS Nano – volume: 12 start-page: 1091 year: 2019 publication-title: Results Phys – start-page: 10 year: 2022 publication-title: APL Mater – volume: 16 start-page: 170 year: 2017 publication-title: Nat. Mater. – volume: 8 start-page: 13 year: 2013 publication-title: Nat. Nanotechonol. – start-page: 1 year: 2021 publication-title: ICAME21, Int. Conf. Adv. Mech. Eng. – volume: 29 start-page: 141 year: 2012 publication-title: IEEE Signal Process. Mag. – ident: e_1_2_8_2_1 doi: 10.1109/TNANO.2002.1005426 – ident: e_1_2_8_38_1 doi: 10.1021/acsami.9b08166 – ident: e_1_2_8_10_1 doi: 10.1038/nmat4703 – ident: e_1_2_8_45_1 doi: 10.1038/nnano.2017.110 – ident: e_1_2_8_65_1 doi: 10.1016/j.colsurfa.2005.11.052 – year: 2023 ident: e_1_2_8_52_1 publication-title: Jpn. J. Appl. Phys. – start-page: 10 year: 2022 ident: e_1_2_8_9_1 publication-title: APL Mater – ident: e_1_2_8_58_1 doi: 10.1021/ja202178k – ident: e_1_2_8_7_1 doi: 10.1038/s41565-018-0302-0 – ident: e_1_2_8_12_1 doi: 10.1038/s41928-020-00473-w – ident: e_1_2_8_22_1 doi: 10.1016/j.mejo.2012.10.001 – ident: e_1_2_8_1_1 doi: 10.1109/5.752515 – ident: e_1_2_8_37_1 doi: 10.1021/acsnano.1c00002 – ident: e_1_2_8_47_1 doi: 10.1002/advs.202101390 – start-page: 6, 619 year: 2023 ident: e_1_2_8_68_1 publication-title: Nat. Electron. – ident: e_1_2_8_35_1 doi: 10.1038/nmat3778 – ident: e_1_2_8_60_1 doi: 10.1021/jp3041204 – ident: e_1_2_8_50_1 doi: 10.1126/science.278.5336.252 – ident: e_1_2_8_17_1 doi: 10.1002/adma.202002092 – ident: e_1_2_8_63_1 – ident: e_1_2_8_64_1 doi: 10.1021/ja207751w – ident: e_1_2_8_78_1 doi: 10.1109/MSP.2012.2211477 – ident: e_1_2_8_4_1 doi: 10.1109/TED.2012.2193129 – ident: e_1_2_8_51_1 doi: 10.1021/acs.jpcc.2c09086 – ident: e_1_2_8_48_1 doi: 10.1002/smtd.202200646 – ident: e_1_2_8_36_1 doi: 10.1021/acs.nanolett.2c00922 – ident: e_1_2_8_16_1 doi: 10.1038/s41427-018-0101-y – ident: e_1_2_8_11_1 doi: 10.1038/s41377-019-0151-0 – year: 2011 ident: e_1_2_8_69_1 publication-title: Int. Electron Devices Meeting – ident: e_1_2_8_49_1 doi: 10.1038/nnano.2012.238 – ident: e_1_2_8_72_1 doi: 10.1038/nature14441 – start-page: 156 year: 2013 ident: e_1_2_8_20_1 publication-title: IEEE Int. Sympo. Inf. Theory – ident: e_1_2_8_5_1 doi: 10.1002/adfm.201600680 – ident: e_1_2_8_15_1 doi: 10.1007/s40747-021-00282-4 – ident: e_1_2_8_59_1 doi: 10.1126/science.1137149 – year: 2023 ident: e_1_2_8_23_1 publication-title: Adv. Funct. Mater. – ident: e_1_2_8_26_1 doi: 10.1109/TED.2012.2225147 – ident: e_1_2_8_61_1 doi: 10.3390/ma13225089 – ident: e_1_2_8_31_1 doi: 10.1007/s00339-011-6294-3 – ident: e_1_2_8_46_1 doi: 10.1038/ncomms7324 – ident: e_1_2_8_28_1 doi: 10.1002/adfm.201303520 – ident: e_1_2_8_8_1 doi: 10.1016/j.mtnano.2023.100319 – ident: e_1_2_8_29_1 doi: 10.1002/adfm.201808376 – ident: e_1_2_8_33_1 doi: 10.1021/acsnano.8b00538 – ident: e_1_2_8_44_1 doi: 10.1109/TED.2017.2776085 – ident: e_1_2_8_6_1 – ident: e_1_2_8_34_1 doi: 10.1038/nnano.2014.150 – ident: e_1_2_8_41_1 doi: 10.1038/s41467-020-15144-9 – ident: e_1_2_8_55_1 doi: 10.1021/ja900773h – ident: e_1_2_8_74_1 doi: 10.1016/0169-7439(93)80052-J – ident: e_1_2_8_27_1 doi: 10.1002/aisy.202000149 – ident: e_1_2_8_66_1 doi: 10.1021/nl9021094 – ident: e_1_2_8_77_1 doi: 10.1038/s42256-019-0089-1 – ident: e_1_2_8_14_1 doi: 10.1002/aelm.202100558 – ident: e_1_2_8_75_1 doi: 10.1038/s41467-018-04484-2 – ident: e_1_2_8_43_1 doi: 10.1109/LED.2011.2163697 – ident: e_1_2_8_30_1 doi: 10.1002/adma.201102395 – ident: e_1_2_8_53_1 doi: 10.1016/j.orgel.2011.08.017 – ident: e_1_2_8_70_1 doi: 10.1016/j.rinp.2018.12.092 – ident: e_1_2_8_67_1 doi: 10.1021/acsnano.6b07159 – ident: e_1_2_8_71_1 doi: 10.1002/aelm.201600326 – ident: e_1_2_8_18_1 doi: 10.1007/s40820-023-01035-3 – ident: e_1_2_8_79_1 doi: 10.1088/0957-4484/21/7/075501 – ident: e_1_2_8_21_1 doi: 10.1021/acs.nanolett.5b02190 – ident: e_1_2_8_76_1 doi: 10.1023/A:1018966222807 – ident: e_1_2_8_24_1 doi: 10.1002/adma.201302047 – ident: e_1_2_8_73_1 doi: 10.1038/nnano.2012.240 – ident: e_1_2_8_54_1 doi: 10.1021/jp204340w – start-page: 1 year: 2021 ident: e_1_2_8_3_1 publication-title: ICAME21, Int. Conf. Adv. Mech. Eng. – ident: e_1_2_8_32_1 doi: 10.1039/D0NA00100G – ident: e_1_2_8_42_1 doi: 10.1021/acsami.2c11016 – ident: e_1_2_8_40_1 doi: 10.1002/adma.201405110 – ident: e_1_2_8_57_1 doi: 10.1021/cm5014784 – ident: e_1_2_8_13_1 doi: 10.1038/s41467-023-39033-z – start-page: 64 year: 2013 ident: e_1_2_8_25_1 publication-title: IEEE/ACM Int. Symp. Nanoscale Arch. (NANOARCH) – ident: e_1_2_8_39_1 doi: 10.1021/nl503897h – ident: e_1_2_8_62_1 doi: 10.1021/jacs.8b13370 – ident: e_1_2_8_19_1 doi: 10.1002/admt.202200193 – ident: e_1_2_8_56_1 doi: 10.1021/acs.nanolett.8b01294 |
| SSID | ssj0017734 |
| Score | 2.4525354 |
| Snippet | Miniaturization of individual selectors in crossbar‐array‐based artificial neural networks is essential for the advancement of the underlying neuromorphic... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Index Database Publisher |
| SubjectTerms | Arrays artificial neural network Artificial neural networks Atomic radius Configurations crossbar array Heterojunctions Heterostructures Learning Mathematical analysis molecular heterojunction molecular tilt configuration molecular‐scale selector Molybdenum disulfide Neural networks Pattern recognition Selectors |
| Title | Tilt‐Engineered Molecular‐Scale Selector for Enhanced Learning in Artificial Neural Networks |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fadfm.202311103 https://www.proquest.com/docview/3040187449 |
| Volume | 34 |
| WOSCitedRecordID | wos001133196600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library - Journals customDbUrl: eissn: 1616-3028 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017734 issn: 1616-301X databaseCode: DRFUL dateStart: 20010101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV29TsMwED5BywAD_4hCQR6QmKImthM3Y0VbMdAK0VbqFuzEhkoooKYw8wg8I0-C7fy0nZBgSnJSnOh8Z39nn78DuPJDFZjkJ4cQKh0qWegI5icO5jHVRu2pWFme2Ts2HLan0_B-5RR_zg9RLbgZz7DjtXFwLrLWkjSUJ8qcJNf4RM9gZBPqWBsvrUG9-9Cf3FU7CYzlO8uBZ3K8vGlJ3Oji1noL6xPTEm2uYlY76fT3_v-7-7BbAE7UyS3kADZkegg7KzSER_A4nr0svj-_SqFM0KCsmqvFI92NEo1kvsCPNMpFvfTZZg6ggp71Cc1S-4mcjwIZyg97sTnm2TFM-r3xza1TVF5wYswwcRIRUMGw8D2BBVPKVW0aupwSgVnoqiCQno6DtFyHP8QwtnOllK-xDQ9Z7HJMTqCWvqbyFJBwhUulJ3T4i6k2W64BCaciIImSiZ_4Dbgu1R695QQbUU6ljCOjs6jSWQOaZa9EhaNlEdGDkCkrSMMGYKv_X1qJOt3-oHo6-8tL57Ct74v8nSbUFvN3eQFb8cdils0vCwP8AUF23Nc |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1dS8MwFL3oJqgPfovTqXkQfCprk7RZH4dbmdgNcRvsrTZtogOpsk2f_Qn-Rn-JSb-2PQniU-mFpOXem-QkOTkBuLJd6Wjyk0EIFQYVzDU4s2MDhxFVSW3JSKY6sz7r95vjsXufswn1WZhMH6JccNMtI-2vdQPXC9KNhWpoGEt9lFwBFDWEkXWoUpVLdgWq7Qdv5JdbCYxlW8uOpUle1rhQbjRxY7WG1ZFpATeXQWs66ni7__C_e7CTQ07UynJkH9ZEcgDbS0KEh_A4nLzMvz-_CqOIUa-4N1eZByqQAg1EtsSPFM5FneQ55Q6gXKD1CU2S9BOZIgXSoh_pI2WZz45g5HWGN10jv3vBiDDDxIi5QznD3LY45kxKUzapa4aUcMxcUzqOsNRMSNnVBIhozfZQSmkrdBO6LDJDTI6hkrwm4gQQN7lJhcXVBBhTlbihgiQh5Q6JpYjt2K7BdeH34C2T2AgyMWUcaJ8Fpc9qUC_CEuRNbRYQ1Q3piwWpWwOcBuCXWoJW2-uVb6d_KXQJm91hzw_82_7dGWwpe87mqUNlPn0X57ARfcwns-lFno0_h4Dgxw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEB60FdGDb7FaNQfB0-Jukt00x2K7KLal2BZ6Wze7iRZkLW317E_wN_pLTPbRx0kQT8sOJLtMZjLfJJMvAFcuV54pfrIIodKiknFLMDe2cBhRbdSOilTKM9tinU5tOOTdvJrQnIXJ-CHmC27GM9L52ji4HMfqZsEaGsbKHCXXAEWHMLIOZepyT_tmufHoD1rzrQTGsq1lzzFFXs6wYG608c1qD6uRaQE3l0FrGnX83X_43z3YySEnqmc2sg9rMjmA7SUiwkN46o9eZ9-fX4VQxqhd3JurxT09kBL1ZLbEjzTORc3kJa0dQDlB6zMaJeknMkYKZEg_0kdaZT49goHf7N_eWfndC1aEGSZWLDwqGBauI7BgStmqRrkdUiIw47byPOnoTEjLdQJEDGd7qJRyNboJOYvsEJNjKCVviTwBJGxhU-kInQBjqg031JAkpMIjsZKxG7sVuC70Howzio0gI1PGgdFZMNdZBarFsAS5q00Doqchc7Eg5RXA6QD80ktQb_jt-dvpXxpdwma34Qet-87DGWxpcV7MU4XSbPIuz2Ej-piNppOL3Bh_AD_F4EI |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Tilt%E2%80%90Engineered+Molecular%E2%80%90Scale+Selector+for+Enhanced+Learning+in+Artificial+Neural+Networks&rft.jtitle=Advanced+functional+materials&rft.au=Jung+Sun+Eo&rft.au=Shin%2C+Jaeho&rft.au=Jeon%2C+Takgyeong&rft.au=Jang%2C+Jingon&rft.date=2024-04-01&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=1616-301X&rft.eissn=1616-3028&rft.volume=34&rft.issue=16&rft_id=info:doi/10.1002%2Fadfm.202311103&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1616-301X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1616-301X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1616-301X&client=summon |