PRIME: Physics-Related Intelligent Mixture of Experts for Transistor Characteristics Prediction
On the map: Mixture-of-experts → Expert routing strategies
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts. Builds on 3 of its 24 references that are in the map. First posted on arXiv 10 May 2025.
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- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
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