PRIME: Physics-Related Intelligent Mixture of Experts for Transistor Characteristics Prediction

• URL: http://arxiv.org/abs/2505.11523v1
• Date: Sat, 10 May 2025 15:53:12 GMT
• Title: PRIME: Physics-Related Intelligent Mixture of Experts for Transistor Characteristics Prediction
• Authors: Zhenxing Dou, Yijiao Wang, Tao Zou, Zhiwei Chen, Fei Liu, Peng Wang, Weisheng Zhao,
• Abstract summary: PRIME (Physics-Related Intelligent Mixture of Experts) is proposed to capture and integrate complex regional characteristics.<n>In essence, our framework incorporates physics-based knowledge with data-driven intelligence.<n>Extensive evaluations are conducted on various gate-all-around (GAA) structures to examine the effectiveness of PRIME.
• Score: 11.03749258060248
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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