Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications
Conventional techniques for extracting physics‐based model parameters are inherently slow processes and often yield less accurate model parameters because of the inflexibility of physical equations. This study presents a novel machine learning–based method to accelerate and enhance the accuracy of c...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400571 |
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| author | Seungjoon Eom Seunghwan Lee Hyeok Yun Kyeongrae Cho Soomin Kim Rockhyun Baek |
| author_facet | Seungjoon Eom Seunghwan Lee Hyeok Yun Kyeongrae Cho Soomin Kim Rockhyun Baek |
| author_sort | Seungjoon Eom |
| collection | DOAJ |
| description | Conventional techniques for extracting physics‐based model parameters are inherently slow processes and often yield less accurate model parameters because of the inflexibility of physical equations. This study presents a novel machine learning–based method to accelerate and enhance the accuracy of compact model generation for multiple devices simultaneously. By integrating a Berkeley short‐channel Insulated‐Gate Field‐Effect Transistor (IGFET) model‐common multigate (BSIM) model with an error‐correction neural network, the proposed approach refines predictions for critical electrical behaviors such as drain current and gate capacitance. Extraction networks dynamically optimize parameter sets for both models, eliminating manual tuning and reducing the need for separate training for each device. The method was validated using TCAD‐simulated 3 nm nanosheet field‐effect transistors devices, achieving a mean absolute percentage error of 1.8% for drain current, 2.8% for transconductance, 8.5% for output conductance, and 0.4% for capacitances. Compared with the BSIM model alone, error reductions of 75, 70, 39, 85, and 81%, respectively, were achieved. This approach showed significant error reductions compared to the BSIM model alone and demonstrated robust performance across devices with variations, proving its effectiveness for large‐scale applications. |
| format | Article |
| id | doaj-art-97d610481a1e4febbd6356d567e12867 |
| institution | OA Journals |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-97d610481a1e4febbd6356d567e128672025-08-20T02:32:56ZengWileyAdvanced Intelligent Systems2640-45672025-05-0175n/an/a10.1002/aisy.202400571Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice ApplicationsSeungjoon Eom0Seunghwan Lee1Hyeok Yun2Kyeongrae Cho3Soomin Kim4Rockhyun Baek5Department of Electrical Engineering Pohang University of Science and Technology Pohang 37673 Republic of KoreaDepartment of Electrical Engineering Pohang University of Science and Technology Pohang 37673 Republic of KoreaDepartment of Electrical Engineering Pohang University of Science and Technology Pohang 37673 Republic of KoreaDepartment of Electrical Engineering Pohang University of Science and Technology Pohang 37673 Republic of KoreaDepartment of Electrical Engineering Pohang University of Science and Technology Pohang 37673 Republic of KoreaDepartment of Electrical Engineering Pohang University of Science and Technology Pohang 37673 Republic of KoreaConventional techniques for extracting physics‐based model parameters are inherently slow processes and often yield less accurate model parameters because of the inflexibility of physical equations. This study presents a novel machine learning–based method to accelerate and enhance the accuracy of compact model generation for multiple devices simultaneously. By integrating a Berkeley short‐channel Insulated‐Gate Field‐Effect Transistor (IGFET) model‐common multigate (BSIM) model with an error‐correction neural network, the proposed approach refines predictions for critical electrical behaviors such as drain current and gate capacitance. Extraction networks dynamically optimize parameter sets for both models, eliminating manual tuning and reducing the need for separate training for each device. The method was validated using TCAD‐simulated 3 nm nanosheet field‐effect transistors devices, achieving a mean absolute percentage error of 1.8% for drain current, 2.8% for transconductance, 8.5% for output conductance, and 0.4% for capacitances. Compared with the BSIM model alone, error reductions of 75, 70, 39, 85, and 81%, respectively, were achieved. This approach showed significant error reductions compared to the BSIM model alone and demonstrated robust performance across devices with variations, proving its effectiveness for large‐scale applications.https://doi.org/10.1002/aisy.202400571compact modelshypernetworksmachine learningnanosheet field‐effect transistorsvariabilities |
| spellingShingle | Seungjoon Eom Seunghwan Lee Hyeok Yun Kyeongrae Cho Soomin Kim Rockhyun Baek Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications Advanced Intelligent Systems compact models hypernetworks machine learning nanosheet field‐effect transistors variabilities |
| title | Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications |
| title_full | Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications |
| title_fullStr | Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications |
| title_full_unstemmed | Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications |
| title_short | Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications |
| title_sort | machine learning driven extraction of hybrid compact models integrating neural networks and berkeley short channel insulated gate field effect transistor model common multigate for multidevice applications |
| topic | compact models hypernetworks machine learning nanosheet field‐effect transistors variabilities |
| url | https://doi.org/10.1002/aisy.202400571 |
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