Binarized Neural Network Comprising Quasi‐Nonvolatile Memory Devices for Neuromorphic Computing
This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec−1) and a high on/off ratio (≥ 107). A pair of QNVM devices are used for a single syna...
Uložené v:
| Vydané v: | Advanced electronic materials Ročník 10; číslo 9 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Seoul
John Wiley & Sons, Inc
01.09.2024
Wiley-VCH |
| Predmet: | |
| ISSN: | 2199-160X, 2199-160X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec−1) and a high on/off ratio (≥ 107). A pair of QNVM devices are used for a single synaptic cell in a cell array, in which its memory state represents the synaptic weight, and the voltages applied to the pair act as input in a complementary fashion. The array of synaptic cells performs matrix multiply‐accumulate (MAC) operations between the weight matrix and input vector using XNOR and current summation. All the results of the MAC operations and vector‐matrix multiplications are equivalent. Moreover, the BNN features a high accuracy of 93.32% in the MNIST image recognition simulation owing to high device uniformity (1.35%), which demonstrates the feasibility of compact and high‐performance neuromorphic computing.
In this study, quasi‐nonvolatile memory devices with the p+‐n‐p‐n+ structure are proposed as synaptic devices. The 2×2 synaptic cell array demonstrates the matrix multiply‐accumulate operations using XNOR and current summations. Furthermore, binarized neural network achieves high accuracy (93.32%) in MNIST image recognition simulation. Consequently, the devices provide synaptic cell array for high‐performance compact neuromorphic computing architecture. |
|---|---|
| AbstractList | Abstract This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec−1) and a high on/off ratio (≥ 107). A pair of QNVM devices are used for a single synaptic cell in a cell array, in which its memory state represents the synaptic weight, and the voltages applied to the pair act as input in a complementary fashion. The array of synaptic cells performs matrix multiply‐accumulate (MAC) operations between the weight matrix and input vector using XNOR and current summation. All the results of the MAC operations and vector‐matrix multiplications are equivalent. Moreover, the BNN features a high accuracy of 93.32% in the MNIST image recognition simulation owing to high device uniformity (1.35%), which demonstrates the feasibility of compact and high‐performance neuromorphic computing. This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec−1) and a high on/off ratio (≥ 107). A pair of QNVM devices are used for a single synaptic cell in a cell array, in which its memory state represents the synaptic weight, and the voltages applied to the pair act as input in a complementary fashion. The array of synaptic cells performs matrix multiply‐accumulate (MAC) operations between the weight matrix and input vector using XNOR and current summation. All the results of the MAC operations and vector‐matrix multiplications are equivalent. Moreover, the BNN features a high accuracy of 93.32% in the MNIST image recognition simulation owing to high device uniformity (1.35%), which demonstrates the feasibility of compact and high‐performance neuromorphic computing. This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec−1) and a high on/off ratio (≥ 107). A pair of QNVM devices are used for a single synaptic cell in a cell array, in which its memory state represents the synaptic weight, and the voltages applied to the pair act as input in a complementary fashion. The array of synaptic cells performs matrix multiply‐accumulate (MAC) operations between the weight matrix and input vector using XNOR and current summation. All the results of the MAC operations and vector‐matrix multiplications are equivalent. Moreover, the BNN features a high accuracy of 93.32% in the MNIST image recognition simulation owing to high device uniformity (1.35%), which demonstrates the feasibility of compact and high‐performance neuromorphic computing. In this study, quasi‐nonvolatile memory devices with the p+‐n‐p‐n+ structure are proposed as synaptic devices. The 2×2 synaptic cell array demonstrates the matrix multiply‐accumulate operations using XNOR and current summations. Furthermore, binarized neural network achieves high accuracy (93.32%) in MNIST image recognition simulation. Consequently, the devices provide synaptic cell array for high‐performance compact neuromorphic computing architecture. This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec −1 ) and a high on/off ratio (≥ 10 7 ). A pair of QNVM devices are used for a single synaptic cell in a cell array, in which its memory state represents the synaptic weight, and the voltages applied to the pair act as input in a complementary fashion. The array of synaptic cells performs matrix multiply‐accumulate (MAC) operations between the weight matrix and input vector using XNOR and current summation. All the results of the MAC operations and vector‐matrix multiplications are equivalent. Moreover, the BNN features a high accuracy of 93.32% in the MNIST image recognition simulation owing to high device uniformity (1.35%), which demonstrates the feasibility of compact and high‐performance neuromorphic computing. |
| Author | Shin, Yunwoo Jeon, Juhee Cho, Kyoungah Kim, Sangsig |
| Author_xml | – sequence: 1 givenname: Yunwoo orcidid: 0000-0002-0090-366X surname: Shin fullname: Shin, Yunwoo organization: Korea University – sequence: 2 givenname: Juhee orcidid: 0000-0003-3633-2288 surname: Jeon fullname: Jeon, Juhee organization: Korea University – sequence: 3 givenname: Kyoungah orcidid: 0000-0003-1122-8003 surname: Cho fullname: Cho, Kyoungah email: chochem@korea.ac.kr organization: Korea University – sequence: 4 givenname: Sangsig orcidid: 0000-0002-7246-8724 surname: Kim fullname: Kim, Sangsig email: sangsig@korea.ac.kr organization: Korea University |
| BookMark | eNqFkc9uEzEQxi1UJErplfNKnBP8b9f2sYQWKqVFSCBxs2bt2eKwWQd7t1U49RH6jDwJToIqhIQ4zWg0v29G3_ecHA1xQEJeMjpnlPLXgP16zimXlNKGPSHHnBkzYw39cvRH_4yc5rwqK0w1QtbimMCbMEAKP9BX1zgl6EsZ72L6Vi3iepNCDsNN9XGCHH7eP1zH4Tb2MIYeqytcx7St3uJtcJirLqa9QCzTzdfg9vg0FvoFedpBn_H0dz0hny_OPy3ez5Yf3l0uzpYzJ5mqZ87oBnTbOs6pNB690WiUcMZA0ymBQLkwrQOJvAbNNSomStNB62unWi9OyOVB10dY2fL6GtLWRgh2P4jpxkIag-vRSm4keAStJJe889rzTmODDWAxRu20Xh20Nil-nzCPdhWnNJT3reCqlorXNS9b88OWSzHnhN3jVUbtLhW7S8U-plIA-RfgwljsjMOYIPT_xsQBuyvGb_9zxJ6dL6-UrsUvBf6lPA |
| CitedBy_id | crossref_primary_10_1016_j_nanoen_2025_111208 crossref_primary_10_1002_adma_202413916 |
| Cites_doi | 10.1016/j.neucom.2017.09.046 10.1002/admt.202000915 10.1109/TNNLS.2017.2778940 10.1038/s41598-019-51814-5 10.1007/s12559-020-09794-6 10.1109/LED.2021.3063954 10.1016/j.sse.2017.11.012 10.1109/LED.2021.3125966 10.3389/fnins.2021.644604 10.3390/electronics10212600 10.1038/s41598-022-07374-2 10.1038/s41586-021-04196-6 10.1109/ACCESS.2021.3121011 10.1038/s41928-019-0288-0 10.1007/s10462-023-10464-w 10.3390/electronics8060661 10.1063/5.0073284 10.1038/s41928-018-0054-8 10.3390/electronics10172181 10.1109/TED.2019.2939393 10.3390/electronics11091421 10.1016/j.patcog.2020.107281 10.1038/s41586-018-0180-5 |
| ContentType | Journal Article |
| Copyright | 2024 The Author(s). Advanced Electronic Materials published by Wiley‐VCH GmbH 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2024 The Author(s). Advanced Electronic Materials published by Wiley‐VCH GmbH – notice: 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | 24P AAYXX CITATION JQ2 DOA |
| DOI | 10.1002/aelm.202400061 |
| DatabaseName | Wiley Online Library Open Access (Activated by CARLI) CrossRef ProQuest Computer Science Collection DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef ProQuest Computer Science Collection |
| DatabaseTitleList | ProQuest Computer Science Collection CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2199-160X |
| EndPage | n/a |
| ExternalDocumentID | oai_doaj_org_article_4294adea874242fd8d2f8e6e6ae1767d 10_1002_aelm_202400061 AELM785 |
| Genre | article |
| GrantInformation_xml | – fundername: Korea University – fundername: Samsung Electronics funderid: IO201223‐08257‐01 – fundername: National Research Foundation of Korea funderid: 2020R1A2C3004538; 2022M3I7A3046571; 2023RS00260876 – fundername: Ministry of Science, ICT & Future Planning funderid: Brain Korea 21 Plus Project |
| GroupedDBID | 0R~ 1OC 24P 33P AAESR AAHHS AAXRX AAZKR ABCUV ACAHQ ACCFJ ACCMX ACCZN ACGFS ACPOU ACXBN ACXQS ADBBV ADKYN ADOZA ADXAS ADZMN ADZOD AEEZP AENEX AEQDE AFBPY AIACR AIURR AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN AMYDB ARCSS AVUZU AZVAB BFHJK BMXJE BRXPI DCZOG EBS EJD GODZA GROUPED_DOAJ LATKE LEEKS LITHE LOXES LUTES LYRES MEWTI O9- P2W ROL SUPJJ WBKPD WOHZO WXSBR ZZTAW AAFWJ AAMMB AAYXX ABJNI ADMLS AEFGJ AFPKN AGXDD AIDQK AIDYY CITATION M~E JQ2 |
| ID | FETCH-LOGICAL-c4175-c986a8bbc22049ded98e973c99a6f73ea0239bca4e25a828e7135a8fabd5c7bd3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001233419800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2199-160X |
| IngestDate | Mon Nov 10 04:31:41 EST 2025 Wed Nov 26 12:51:52 EST 2025 Sat Nov 29 07:18:13 EST 2025 Tue Nov 18 21:45:43 EST 2025 Wed Jan 22 17:13:29 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| License | Attribution |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4175-c986a8bbc22049ded98e973c99a6f73ea0239bca4e25a828e7135a8fabd5c7bd3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-7246-8724 0000-0002-0090-366X 0000-0003-3633-2288 0000-0003-1122-8003 |
| OpenAccessLink | https://doaj.org/article/4294adea874242fd8d2f8e6e6ae1767d |
| PQID | 3275472552 |
| PQPubID | 6852865 |
| PageCount | 7 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_4294adea874242fd8d2f8e6e6ae1767d proquest_journals_3275472552 crossref_primary_10_1002_aelm_202400061 crossref_citationtrail_10_1002_aelm_202400061 wiley_primary_10_1002_aelm_202400061_AELM785 |
| PublicationCentury | 2000 |
| PublicationDate | September 2024 2024-09-00 20240901 2024-09-01 |
| PublicationDateYYYYMMDD | 2024-09-01 |
| PublicationDate_xml | – month: 09 year: 2024 text: September 2024 |
| PublicationDecade | 2020 |
| PublicationPlace | Seoul |
| PublicationPlace_xml | – name: Seoul |
| PublicationTitle | Advanced electronic materials |
| PublicationYear | 2024 |
| Publisher | John Wiley & Sons, Inc Wiley-VCH |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: Wiley-VCH |
| References | 2019; 8 2021; 9 2018; 29 2019; 9 2018; 143 2021; 43 2021; 42 2023; 56 2019; 2 2020; 105 2021; 13 2022; 120 2018; 275 2021; 15 2020; 5 2021; 10 2020; 2 2018; 1 2019; 66 2018; 558 2022; 12 2019 2018 2017 2022; 11 2016; 29 2022; 601 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_11_1 e_1_2_9_10_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 e_1_2_9_15_1 e_1_2_9_14_1 e_1_2_9_17_1 e_1_2_9_16_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_21_1 e_1_2_9_24_1 e_1_2_9_23_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_2_1 e_1_2_9_1_1 Zhao W. (e_1_2_9_3_1) 2020; 2 e_1_2_9_9_1 e_1_2_9_26_1 e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_27_1 e_1_2_9_29_1 |
| References_xml | – volume: 29 start-page: 4782 year: 2018 publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 10 start-page: 2181 year: 2021 publication-title: Electronics. – volume: 66 start-page: 4753 year: 2019 publication-title: IEEE Trans. Electron Devices – volume: 558 start-page: 60 year: 2018 publication-title: Nature – volume: 2 start-page: 25 year: 2020 publication-title: Air Space Syst – volume: 601 start-page: 211 year: 2022 publication-title: Nature – volume: 11 start-page: 1421 year: 2022 publication-title: Electronics – volume: 5 year: 2020 publication-title: Adv. Mater. Technol. – volume: 9 year: 2019 publication-title: Sci. Rep. – year: 2018 – volume: 1 start-page: 246 year: 2018 publication-title: Nat. Electron. – volume: 56 year: 2023 publication-title: Artif. Intell. Rev. – volume: 15 year: 2021 publication-title: Front. Neurosci. – year: 2018 article-title: presented at – volume: 275 start-page: 1072 year: 2018 publication-title: Neurocomputing – volume: 43 start-page: 142 year: 2021 publication-title: IEEE Electron Device Lett. – volume: 2 start-page: 420 year: 2019 publication-title: Nat. Electron. – volume: 13 start-page: 179 year: 2021 publication-title: Cogn. Comput. – volume: 12 start-page: 3516 year: 2022 publication-title: Sci. Rep. – volume: 10 start-page: 2600 year: 2021 publication-title: Electronics – volume: 29 year: 2016 – year: 2017 – volume: 42 start-page: 649 year: 2021 publication-title: IEEE Electron Device Lett. – year: 2019 – volume: 105 year: 2020 publication-title: Pattern Recognit – volume: 120 year: 2022 publication-title: Appl. Phys. Lett. – volume: 143 start-page: 10 year: 2018 publication-title: Solid State Electron. – volume: 8 start-page: 661 year: 2019 publication-title: Electronics – volume: 9 year: 2021 publication-title: IEEE Access – ident: e_1_2_9_6_1 doi: 10.1016/j.neucom.2017.09.046 – ident: e_1_2_9_31_1 doi: 10.1002/admt.202000915 – ident: e_1_2_9_15_1 doi: 10.1109/TNNLS.2017.2778940 – ident: e_1_2_9_10_1 doi: 10.1038/s41598-019-51814-5 – ident: e_1_2_9_20_1 doi: 10.1007/s12559-020-09794-6 – ident: e_1_2_9_26_1 doi: 10.1109/LED.2021.3063954 – ident: e_1_2_9_29_1 doi: 10.1016/j.sse.2017.11.012 – ident: e_1_2_9_17_1 doi: 10.1109/LED.2021.3125966 – ident: e_1_2_9_28_1 doi: 10.3389/fnins.2021.644604 – ident: e_1_2_9_9_1 doi: 10.3390/electronics10212600 – ident: e_1_2_9_27_1 doi: 10.1038/s41598-022-07374-2 – ident: e_1_2_9_12_1 doi: 10.1038/s41586-021-04196-6 – ident: e_1_2_9_19_1 doi: 10.1109/ACCESS.2021.3121011 – ident: e_1_2_9_16_1 – ident: e_1_2_9_18_1 doi: 10.1038/s41928-019-0288-0 – ident: e_1_2_9_4_1 – ident: e_1_2_9_32_1 – ident: e_1_2_9_2_1 doi: 10.1007/s10462-023-10464-w – ident: e_1_2_9_24_1 – ident: e_1_2_9_7_1 doi: 10.3390/electronics8060661 – ident: e_1_2_9_11_1 doi: 10.1063/5.0073284 – ident: e_1_2_9_14_1 doi: 10.1038/s41928-018-0054-8 – ident: e_1_2_9_8_1 – volume: 2 start-page: 25 year: 2020 ident: e_1_2_9_3_1 publication-title: Air Space Syst – ident: e_1_2_9_30_1 – ident: e_1_2_9_22_1 – ident: e_1_2_9_23_1 – ident: e_1_2_9_21_1 doi: 10.3390/electronics10172181 – ident: e_1_2_9_25_1 doi: 10.1109/TED.2019.2939393 – ident: e_1_2_9_5_1 doi: 10.3390/electronics11091421 – ident: e_1_2_9_1_1 doi: 10.1016/j.patcog.2020.107281 – ident: e_1_2_9_13_1 doi: 10.1038/s41586-018-0180-5 |
| SSID | ssj0001763453 |
| Score | 2.3063395 |
| Snippet | This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and... Abstract This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop... |
| SourceID | doaj proquest crossref wiley |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| SubjectTerms | Accuracy Arrays binarized neural network Energy consumption Feedback loops image recognition Memory devices multiply‐accumulate Neural networks Neuromorphic computing Positive feedback positive feedback loop quasi‐nonvolatile memory Silicon |
| SummonAdditionalLinks | – databaseName: Wiley Online Library Open Access (Activated by CARLI) dbid: 24P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NbtQwELZQ4cCFggCxpSAfkLgQNbGd2D620IpDuyoSoL1ZE3sCK612q90WqZx4BJ6RJ2HGyW67B4QQV2diWZ7_ZOYbIV51FZTQUFrijdeFQa1J51RTdN7EWmNtff7R_vnUjsduMvHnt7r4e3yIzQc31oxsr1nBoV0d3ICGAs64k5xrIEvOf-5WlbYs18qc33xlIfUxGYqSNNMXVVNO1siNpTrY3mLLM2UA_62o83bsmp3Pye7_H_uheDAEnvKwl5RH4g7OHws44nbc6XdMklE66Pm4LwuXbCdI_8mxyQ9XsJr--vFzvJiTLSNOzlCecYHutXyH2dBIinzzBgtavfg6jbIfFkFvPxGfTo4_vn1fDEMXimgolCiidw24to1KUfKQMHmH3uroPTSd1QjcDdtGMKhqoHQNecYfuA7aVEfbJv1U7MwXc3wmZKoqG0s0EHUyTqN3FiC5qGvLEuJGolhfeIgDIjkPxpiFHktZBb6ssLmskXi9ob_osTj-SHnE_NtQMYZ2Xlgsv4RBJQN5YgMJwVlDcUqXXFKdwwYbQBIbm0Zif839MCj2Kmhla2MpD1Mj8Sbz-S9HCYfHp2fW1Xv_Rv5c3Oe1vpptX-xcLq_whbgXv11OV8uXWch_A79d_p4 priority: 102 providerName: Wiley-Blackwell |
| Title | Binarized Neural Network Comprising Quasi‐Nonvolatile Memory Devices for Neuromorphic Computing |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Faelm.202400061 https://www.proquest.com/docview/3275472552 https://doaj.org/article/4294adea874242fd8d2f8e6e6ae1767d |
| Volume | 10 |
| WOSCitedRecordID | wos001233419800001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2199-160X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001763453 issn: 2199-160X databaseCode: DOA dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2199-160X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001763453 issn: 2199-160X databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 2199-160X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001763453 issn: 2199-160X databaseCode: 24P dateStart: 20230101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwELYAceCCWpWK5SUfKvVCRGI7sX3ksagHdkUlqLhZE3siVlrtrlhAooeqP6G_sb-EsZNdLYeKSy85OBPLGs_TGX_D2JemgBwqSkussjJTKCXpnKiyxipfSiy1TT_af1zp4dDc3dnrlVZfsSashQduGXdC9lJBQDCUwynRBBNEY7DCCrDQlQ7R-ubariRT6XSF1EaVcoHSmIsTwHG8eB5LJvOqeOOFElj_mwhzNU5NjubyA9vuIkR-2q7sI1vDyScGZ_He7OgnBh7hNOj9sK3f5lGhSVHJA_HvTzAf_f39ZzidkNEhlo-RD2Il7Qu_wGQROIWoaYIpjc7uR563XR3o6x12e9m_Of-Wdd0RMq_I52femgpMXXshKMoPGKxBq6W3FqpGS4R4bbX2oFCUQHkVxmZ8YBqoQ-l1HeRntjGZTnCX8VAU2ueowMugjERrNEAwXpY6bqXpsWzBLec76PDYwWLsWtBj4SJ33ZK7PfZ1ST9rQTP-SXkWmb-kimDXaYBEwHUi4N4TgR47WGyd6zRw7qTQpdKUMIkeO07b-c5S3Gn_aqBNufc_lrTPtuLEbTHaAdt4fHjCQ7bpnx9H84cjti7U9VGSWHoOfvVfASKZ8MA |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NbhMxELZQQYILPwJEaIE9IHFh1Y3tXdvHFloVkayKVFBu1qw9C5GipEraSnDiEXhGnoQZ7yYlB4SQuNpey_L8e2e-EeJlO4QCKgpLnHYq16gUyZys8tbpUCosjUs_2j-NTF3bycSd9tmEXAvT4UNsHtxYMpK-ZgHnB-n9a9RQwBmXknMSZMEB0E1NpoabGEh9ev3MQvKjExYliabLh1UxWUM3FnJ_e4st05QQ_Lfczt-d12R9ju_9h3PfF3d71zM76HjlgbiB84cCDrkgd_oNY8Y4HTRfd4nhGWsK0gBk2rIPl7Ca_vz-o17MSZsRLWeYjTlF92v2FpOqycj3TRssaPT8yzRkXbsI-vqR-Hh8dPbmJO_bLuRBkzORB2crsE0TpKTwIWJ0Fp1RwTmoWqMQuB62CaBRlkABG3KXP7AtNLEMponqsdiZL-b4RGRxODShQA1BRW0VOmsAog2qNMwjdiDy9Y370GOSc2uMme_QlKXny_KbyxqIV5v15x0axx9XHjIBN6sYRTsNLJaffS-UnmyxhohgjSZPpY02ytZihRUg8Y2JA7G3Jr_vRXvllTSlNhSJyYF4nQj9l6P4g6PR2Njy6b8tfyFun5yNR370rn6_K-7wfJfbtid2LpaX-EzcClcX09XyeeL4X9VuAo8 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NbhMxELZQixAXfgSI0AI-IHFh1Y3tXdvHljYCka6CBCg3a9aeLZGiJEpapPbUR-AZ-ySMvZuUHBBC4mp7Lcvz7535hrE3TR9yKCksscrKTKGUJHOizBqrfCGx0Db9aP821FVlxmM76rIJYy1Miw-xeXCLkpH0dRRwXITm4BY1FHAaS8ljEmQeA6BdVZCijeDOanT7zELyoxIWJYmmzfplPl5DN-biYHuLLdOUEPy33M7fnddkfQYP_8O5H7EHnevJD1teeczu4OwJg6NYkDu5wsAjTgfNV21iOI-agjQAmTb--QJWk5vrn9V8RtqMaDlFfhpTdC_5MSZVw8n3TRvMaXTxfeJ52y6Cvn7Kvg5Ovrz_kHVtFzKvyJnIvDUlmLr2QlD4EDBYg1ZLby2UjZYIsR629qBQFEABG8Yuf2AaqEPhdR3kM7Yzm8_wOeOh39c-RwVeBmUkWqMBgvGSKEU8YnosW9-48x0meWyNMXUtmrJw8bLc5rJ67O1m_aJF4_jjyqNIwM2qiKKdBubLM9cJpSNbrCAgGK3IU2mCCaIxWGIJSHyjQ4_tr8nvOtFeOSl0oTRFYqLH3iVC_-Uo7vBkeKpN8eLflr9m90bHAzf8WH3aY_fjdJvats92zpcX-JLd9T_OJ6vlq8TwvwB3kwIT |
| 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=Binarized+Neural+Network+Comprising+Quasi%E2%80%90Nonvolatile+Memory+Devices+for+Neuromorphic+Computing&rft.jtitle=Advanced+electronic+materials&rft.au=Shin%2C+Yunwoo&rft.au=Jeon%2C+Juhee&rft.au=Cho%2C+Kyoungah&rft.au=Kim%2C+Sangsig&rft.date=2024-09-01&rft.issn=2199-160X&rft.eissn=2199-160X&rft.volume=10&rft.issue=9&rft_id=info:doi/10.1002%2Faelm.202400061&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_aelm_202400061 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2199-160X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2199-160X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2199-160X&client=summon |