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

2025/05/10 by Dou, Zhenxing, Yijiao Wang, Wang, Yijiao +9
Engineering · Materials Science · #Low-power high-performance VLSI design #Advancements in Semiconductor Devices and Circuit Design #Machine Learning in Materials Science

paper · pdf · doi:10.48550/arxiv.2505.11523

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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