Large-Scale Training in Neural Compact Models for Accurate and Adaptable MOSFET Simulation
We address the challenges associated with traditional analytical models, such as BSIM, in semiconductor device modeling. These models often face limitations in accurately representing the complex behaviors of miniaturized devices. As an alternative, Neural Compact Models (NCMs) offer improved modeli...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of the Electron Devices Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10566861/ |
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| author | Chanwoo Park Seungjun Lee Junghwan Park Kyungjin Rim Jihun Park Seonggook Cho Jongwook Jeon Hyunbo Cho |
| author_facet | Chanwoo Park Seungjun Lee Junghwan Park Kyungjin Rim Jihun Park Seonggook Cho Jongwook Jeon Hyunbo Cho |
| author_sort | Chanwoo Park |
| collection | DOAJ |
| description | We address the challenges associated with traditional analytical models, such as BSIM, in semiconductor device modeling. These models often face limitations in accurately representing the complex behaviors of miniaturized devices. As an alternative, Neural Compact Models (NCMs) offer improved modeling capabilities, but their effectiveness is constrained by a reliance on extensive datasets for accurate performance. In real-world scenarios, where measurements for device modeling are often limited, this dependence becomes a significant hindrance. In response, this work presents a large-scale pre-training approach for NCMs. By utilizing extensive datasets across various technology nodes, our method enables NCMs to develop a more detailed understanding of device behavior, thereby enhancing the accuracy and adaptability of MOSFET device simulations, particularly when data availability is limited. Our study illustrates the potential benefits of large-scale pre-training in enhancing the capabilities of NCMs, offering a practical solution to one of the key challenges in current device modeling practices. |
| format | Article |
| id | doaj-art-41bd7e5386234739aa8ec28dc5eec43f |
| institution | OA Journals |
| issn | 2168-6734 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of the Electron Devices Society |
| spelling | doaj-art-41bd7e5386234739aa8ec28dc5eec43f2025-08-20T01:55:19ZengIEEEIEEE Journal of the Electron Devices Society2168-67342024-01-011274575110.1109/JEDS.2024.341752110566861Large-Scale Training in Neural Compact Models for Accurate and Adaptable MOSFET SimulationChanwoo Park0https://orcid.org/0000-0001-7321-0269Seungjun Lee1https://orcid.org/0009-0001-4314-0260Junghwan Park2https://orcid.org/0009-0004-4602-9877Kyungjin Rim3Jihun Park4Seonggook Cho5Jongwook Jeon6https://orcid.org/0000-0002-5232-650XHyunbo Cho7https://orcid.org/0009-0006-0425-5301Research and Development Center, Alsemy Inc., Seoul, South KoreaResearch and Development Center, Alsemy Inc., Seoul, South KoreaResearch and Development Center, Alsemy Inc., Seoul, South KoreaResearch and Development Center, Alsemy Inc., Seoul, South KoreaResearch and Development Center, Alsemy Inc., Seoul, South KoreaResearch and Development Center, Alsemy Inc., Seoul, South KoreaSchool of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon-si, South KoreaResearch and Development Center, Alsemy Inc., Seoul, South KoreaWe address the challenges associated with traditional analytical models, such as BSIM, in semiconductor device modeling. These models often face limitations in accurately representing the complex behaviors of miniaturized devices. As an alternative, Neural Compact Models (NCMs) offer improved modeling capabilities, but their effectiveness is constrained by a reliance on extensive datasets for accurate performance. In real-world scenarios, where measurements for device modeling are often limited, this dependence becomes a significant hindrance. In response, this work presents a large-scale pre-training approach for NCMs. By utilizing extensive datasets across various technology nodes, our method enables NCMs to develop a more detailed understanding of device behavior, thereby enhancing the accuracy and adaptability of MOSFET device simulations, particularly when data availability is limited. Our study illustrates the potential benefits of large-scale pre-training in enhancing the capabilities of NCMs, offering a practical solution to one of the key challenges in current device modeling practices.https://ieeexplore.ieee.org/document/10566861/Compact modelDTCOfoundation modelMOSFETneural network |
| spellingShingle | Chanwoo Park Seungjun Lee Junghwan Park Kyungjin Rim Jihun Park Seonggook Cho Jongwook Jeon Hyunbo Cho Large-Scale Training in Neural Compact Models for Accurate and Adaptable MOSFET Simulation IEEE Journal of the Electron Devices Society Compact model DTCO foundation model MOSFET neural network |
| title | Large-Scale Training in Neural Compact Models for Accurate and Adaptable MOSFET Simulation |
| title_full | Large-Scale Training in Neural Compact Models for Accurate and Adaptable MOSFET Simulation |
| title_fullStr | Large-Scale Training in Neural Compact Models for Accurate and Adaptable MOSFET Simulation |
| title_full_unstemmed | Large-Scale Training in Neural Compact Models for Accurate and Adaptable MOSFET Simulation |
| title_short | Large-Scale Training in Neural Compact Models for Accurate and Adaptable MOSFET Simulation |
| title_sort | large scale training in neural compact models for accurate and adaptable mosfet simulation |
| topic | Compact model DTCO foundation model MOSFET neural network |
| url | https://ieeexplore.ieee.org/document/10566861/ |
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