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An Artificial Neural Network Based on Oxide Synaptic Transistor for Accurate and Robust Image Recognition
Synaptic transistors with low-temperature, solution-processed dielectric films have demonstrated programmable conductance, and therefore potential applications in hardware artificial neural networks for recognizing noisy images. Here, we engineered AlO /InO synaptic transistors via a solution proces...
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| Published in: | Micromachines (Basel) 2024-04, Vol.15 (4), p.433 |
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| container_title | Micromachines (Basel) |
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| creator | Su, Dongyue Liang, Xiaoci Geng, Di Wu, Qian Liu, Baiquan Liu, Chuan |
| description | Synaptic transistors with low-temperature, solution-processed dielectric films have demonstrated programmable conductance, and therefore potential applications in hardware artificial neural networks for recognizing noisy images. Here, we engineered AlO
/InO
synaptic transistors via a solution process to instantiate neural networks. The transistors show long-term potentiation under appropriate gate voltage pulses. The artificial neural network, consisting of one input layer and one output layer, was constructed using 9 × 3 synaptic transistors. By programming the calculated weight, the hardware network can recognize 3 × 3 pixel images of characters z, v and n with a high accuracy of 85%, even with 40% noise. This work demonstrates that metal-oxide transistors, which exhibit significant long-term potentiation of conductance, can be used for the accurate recognition of noisy images. |
| doi_str_mv | 10.3390/mi15040433 |
| format | article |
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/InO
synaptic transistors via a solution process to instantiate neural networks. The transistors show long-term potentiation under appropriate gate voltage pulses. The artificial neural network, consisting of one input layer and one output layer, was constructed using 9 × 3 synaptic transistors. By programming the calculated weight, the hardware network can recognize 3 × 3 pixel images of characters z, v and n with a high accuracy of 85%, even with 40% noise. This work demonstrates that metal-oxide transistors, which exhibit significant long-term potentiation of conductance, can be used for the accurate recognition of noisy images.</description><identifier>ISSN: 2072-666X</identifier><identifier>EISSN: 2072-666X</identifier><identifier>DOI: 10.3390/mi15040433</identifier><identifier>PMID: 38675245</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Aluminum ; artificial neural network ; Artificial neural networks ; Bias ; Electrolytes ; Hardware ; Hydrogen ; image recognition ; Low temperature ; Machine vision ; Metal oxides ; Neural networks ; Silicon wafers ; synaptic transistors ; Temperature ; Thin films ; Transistors ; Voltage pulses</subject><ispartof>Micromachines (Basel), 2024-04, Vol.15 (4), p.433</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c415t-71a310398528c83e02914d13ee3b2187214208eb7d5cd4022b91d20a63e8dbd3</cites><orcidid>0000-0001-9375-7683 ; 0000-0002-0695-592X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3046968882/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3046968882?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,37013,44590,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38675245$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Su, Dongyue</creatorcontrib><creatorcontrib>Liang, Xiaoci</creatorcontrib><creatorcontrib>Geng, Di</creatorcontrib><creatorcontrib>Wu, Qian</creatorcontrib><creatorcontrib>Liu, Baiquan</creatorcontrib><creatorcontrib>Liu, Chuan</creatorcontrib><title>An Artificial Neural Network Based on Oxide Synaptic Transistor for Accurate and Robust Image Recognition</title><title>Micromachines (Basel)</title><addtitle>Micromachines (Basel)</addtitle><description>Synaptic transistors with low-temperature, solution-processed dielectric films have demonstrated programmable conductance, and therefore potential applications in hardware artificial neural networks for recognizing noisy images. Here, we engineered AlO
/InO
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This work demonstrates that metal-oxide transistors, which exhibit significant long-term potentiation of conductance, can be used for the accurate recognition of noisy images.</description><subject>Aluminum</subject><subject>artificial neural network</subject><subject>Artificial neural networks</subject><subject>Bias</subject><subject>Electrolytes</subject><subject>Hardware</subject><subject>Hydrogen</subject><subject>image recognition</subject><subject>Low temperature</subject><subject>Machine vision</subject><subject>Metal oxides</subject><subject>Neural networks</subject><subject>Silicon wafers</subject><subject>synaptic transistors</subject><subject>Temperature</subject><subject>Thin films</subject><subject>Transistors</subject><subject>Voltage pulses</subject><issn>2072-666X</issn><issn>2072-666X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdUV1rFDEUDaLYsvbFHyABX0TYms9J8jgWPxaKhboPvg2Z5M6SdSZZkxm0_95st1YxIbnhcs7JPRyEXlJyybkh76ZAJRFEcP4EnTOi2Lppmm9P_3mfoYtS9qQupUy9nqMzrhslmZDnKLQRt3kOQ3DBjvgLLPm-zD9T_o7f2wIep4hvfgUP-OtdtIc5OLzNNpZQ5pTxUE_rXKXNgG30-Db1S5nxZrI7wLfg0i6GOaT4Aj0b7Fjg4qGu0Pbjh-3V5_X1zafNVXu9doLKea2o5ZRwoyXTTnMgzFDhKQfgPaNaMSoY0dArL50XhLHeUM-IbTho33u-QpuTrE923x1ymGy-65IN3X0j5V1nq183QjeonlRF4mnTCwPODl4x2WtOwYlGHLXenLQOOf1YoMzdFIqDcbQR0lI6ToQyktE67wq9_g-6T0uO1egR1ZhGa80q6vKE2tn6f4hDmrN1dXuYgksRhlD7rTJcSmG4roS3J4LLqZQMw6MjSrpj_t3f_Cv41cMMSz-Bf4T-SZv_BpbiqA0</recordid><startdate>20240401</startdate><enddate>20240401</enddate><creator>Su, Dongyue</creator><creator>Liang, Xiaoci</creator><creator>Geng, Di</creator><creator>Wu, Qian</creator><creator>Liu, Baiquan</creator><creator>Liu, Chuan</creator><general>MDPI AG</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>L7M</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-9375-7683</orcidid><orcidid>https://orcid.org/0000-0002-0695-592X</orcidid></search><sort><creationdate>20240401</creationdate><title>An Artificial Neural Network Based on Oxide Synaptic Transistor for Accurate and Robust Image Recognition</title><author>Su, Dongyue ; 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Here, we engineered AlO
/InO
synaptic transistors via a solution process to instantiate neural networks. The transistors show long-term potentiation under appropriate gate voltage pulses. The artificial neural network, consisting of one input layer and one output layer, was constructed using 9 × 3 synaptic transistors. By programming the calculated weight, the hardware network can recognize 3 × 3 pixel images of characters z, v and n with a high accuracy of 85%, even with 40% noise. This work demonstrates that metal-oxide transistors, which exhibit significant long-term potentiation of conductance, can be used for the accurate recognition of noisy images.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>38675245</pmid><doi>10.3390/mi15040433</doi><orcidid>https://orcid.org/0000-0001-9375-7683</orcidid><orcidid>https://orcid.org/0000-0002-0695-592X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aluminum artificial neural network Artificial neural networks Bias Electrolytes Hardware Hydrogen image recognition Low temperature Machine vision Metal oxides Neural networks Silicon wafers synaptic transistors Temperature Thin films Transistors Voltage pulses |
| title | An Artificial Neural Network Based on Oxide Synaptic Transistor for Accurate and Robust Image Recognition |
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