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Gate\u2010last processed FDSOI MOSFET with a high\u2010k\/metal gate stack features a memory window of 10<jats:sup>3<\/jats:sup>. Synaptic conductance is stably regulated by utilizing the FDSOI MOSFET, which offers the advantage of mitigating leakage current when compared to bulk Si MOSFET. Short\u2010 and long\u2010term plasticity are investigated by applying engineered pulse, verifying the long\u2010term synaptic properties of pattern recognition processes. With controllable synaptic conductance, the trade\u2010off between conductance change and linearity regarding the recognition rate is evaluated, attaining a recognition rate of 0.83. To verify the pre\u2010 and post\u2010synaptic weights within a real hardware\u2010based neuromorphic system, 5\u2009\u00d7\u20096 FDSOI field\u2010effect transistor (FET) synapse array is interconnected to 10\u2009\u00d7\u200910 leaky integrate\u2010and\u2010fire (LIF) neuron array. The synaptic plasticity of FDSOI MOSFET in post\u2010neurons following neuron firing in the neuron device is successfully demonstrated. These results indicate that FDSOI MOSFET devices could be applicable as synapse devices due to controllability and capability to realize signal transmissions and self\u2010learning processes simultaneously and used to mimic a synapse neuron network system by configuring a hardware system interconnected with the LIF neuron.<\/jats:p>","DOI":"10.1002\/aisy.202300754","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T09:56:26Z","timestamp":1708595786000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Synaptic Characteristics of Fully Depleted Silicon\u2010on\u2010Insulator Metal\u2010Oxide\u2010Semiconductor Field\u2010Effect Transistors and Synapse\u2010Neuron Arrayed Neuromorphic Hardware System"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6981-6788","authenticated-orcid":false,"given":"Yu\u2010Rim","family":"Jeon","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering The University of Texas at Austin  Austin TX 78712 USA"}]},{"given":"Jeong\u2010Hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering University of California at San Diego  La Jolla CA 92093 USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7133-5586","authenticated-orcid":false,"given":"Deji","family":"Akinwande","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering The University of Texas at Austin  Austin TX 78712 USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8386-3885","authenticated-orcid":false,"given":"Changhwan","family":"Choi","sequence":"additional","affiliation":[{"name":"Division of Materials Science and Engineering Hanyang University  Seoul 04763 Korea"}]}],"member":"311","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"volume-title":"The Computer and the Brain","year":"1958","author":"von Neumann J.","key":"e_1_2_8_2_1"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/admi.202300290"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1021\/acsami.0c18784"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/adfm.202213296"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/aelm.202300249"},{"key":"e_1_2_8_7_1","first-page":"29","volume":"9","author":"Kwon D.","year":"2023","journal-title":"Appl. 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