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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.11523 |
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| _version_ | 1866915291410202624 |
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| author | Dou, Zhenxing Wang, Yijiao Zou, Tao Chen, Zhiwei Liu, Fei Wang, Peng Zhao, Weisheng |
| author_facet | Dou, Zhenxing Wang, Yijiao Zou, Tao Chen, Zhiwei Liu, Fei Wang, Peng Zhao, Weisheng |
| contents | In recent years, machine learning has been extensively applied to data prediction during process ramp-up, with a particular focus on transistor characteristics for circuit design and manufacture. However, capturing the nonlinear current response across multiple operating regions remains a challenge for neural networks. To address such challenge, a novel machine learning framework, PRIME (Physics-Related Intelligent Mixture of Experts), is proposed to capture and integrate complex regional characteristics. In essence, our framework incorporates physics-based knowledge with data-driven intelligence. By leveraging a dynamic weighting mechanism in its gating network, PRIME adaptively activates the suitable expert model based on distinct input data features. Extensive evaluations are conducted on various gate-all-around (GAA) structures to examine the effectiveness of PRIME and considerable improvements (60\%-84\%) in prediction accuracy are shown over state-of-the-art models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11523 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PRIME: Physics-Related Intelligent Mixture of Experts for Transistor Characteristics Prediction Dou, Zhenxing Wang, Yijiao Zou, Tao Chen, Zhiwei Liu, Fei Wang, Peng Zhao, Weisheng Machine Learning In recent years, machine learning has been extensively applied to data prediction during process ramp-up, with a particular focus on transistor characteristics for circuit design and manufacture. However, capturing the nonlinear current response across multiple operating regions remains a challenge for neural networks. To address such challenge, a novel machine learning framework, PRIME (Physics-Related Intelligent Mixture of Experts), is proposed to capture and integrate complex regional characteristics. In essence, our framework incorporates physics-based knowledge with data-driven intelligence. By leveraging a dynamic weighting mechanism in its gating network, PRIME adaptively activates the suitable expert model based on distinct input data features. Extensive evaluations are conducted on various gate-all-around (GAA) structures to examine the effectiveness of PRIME and considerable improvements (60\%-84\%) in prediction accuracy are shown over state-of-the-art models. |
| title | PRIME: Physics-Related Intelligent Mixture of Experts for Transistor Characteristics Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.11523 |