PRIME: Physics-Related Intelligent Mixture of Experts for Transistor Characteristics Prediction
This work addresses a domain-specific problem in semiconductor manufacturing and circuit design, offering incremental improvements for transistor characteristic prediction.
The paper tackles the challenge of predicting nonlinear transistor characteristics across multiple operating regions by proposing PRIME, a physics-informed mixture of experts framework, which achieves 60%-84% improvements in prediction accuracy over state-of-the-art models.
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.