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Neuron Circuit Based on a Split-gate Transistor with Nonvolatile Memory for Homeostatic Functions of Biological Neurons
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Hansol | - |
| dc.contributor.author | Woo, Sung Yun | - |
| dc.contributor.author | Kim, Hyungjin | - |
| dc.date.accessioned | 2024-11-28T17:00:40Z | - |
| dc.date.available | 2024-11-28T17:00:40Z | - |
| dc.date.issued | 2024-06 | - |
| dc.identifier.issn | 2313-7673 | - |
| dc.identifier.issn | 2313-7673 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/197742 | - |
| dc.description.abstract | To mimic the homeostatic functionality of biological neurons, a split-gate field-effect transistor (S-G FET) with a charge trap layer is proposed within a neuron circuit. By adjusting the number of charges trapped in the Si3N4 layer, the threshold voltage (Vth) of the S-G FET changes. To prevent degradation of the gate dielectric due to program/erase pulses, the gates for read operation and Vth control were separated through the fin structure. A circuit that modulates the width and amplitude of the pulse was constructed to generate a Program/Erase pulse for the S-G FET as the output pulse of the neuron circuit. By adjusting the Vth of the neuron circuit, the firing rate can be lowered by increasing the Vth of the neuron circuit with a high firing rate. To verify the performance of the neural network based on S-G FET, a simulation of online unsupervised learning and classification in a 2-layer SNN is performed. The results show that the recognition rate was improved by 8% by increasing the threshold of the neuron circuit fired. | - |
| dc.format.extent | 14 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | MDPI AG | - |
| dc.title | Neuron Circuit Based on a Split-gate Transistor with Nonvolatile Memory for Homeostatic Functions of Biological Neurons | - |
| dc.type | Article | - |
| dc.publisher.location | 스위스 | - |
| dc.identifier.doi | 10.3390/biomimetics9060335 | - |
| dc.identifier.scopusid | 2-s2.0-85197915304 | - |
| dc.identifier.wosid | 001254573200001 | - |
| dc.identifier.bibliographicCitation | Biomimetics, v.9, no.6, pp 1 - 14 | - |
| dc.citation.title | Biomimetics | - |
| dc.citation.volume | 9 | - |
| dc.citation.number | 6 | - |
| dc.citation.startPage | 1 | - |
| dc.citation.endPage | 14 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Engineering | - |
| dc.relation.journalResearchArea | Materials Science | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
| dc.relation.journalWebOfScienceCategory | Materials Science, Biomaterials | - |
| dc.subject.keywordPlus | NETWORK | - |
| dc.subject.keywordPlus | PLASTICITY | - |
| dc.subject.keywordPlus | DEVICES | - |
| dc.subject.keywordPlus | SYNAPSE | - |
| dc.subject.keywordAuthor | charge trap layer | - |
| dc.subject.keywordAuthor | homeostasis functionality | - |
| dc.subject.keywordAuthor | neuron circuit | - |
| dc.subject.keywordAuthor | nonvolatile memory | - |
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