PRIME模型通过融合物理知识与数据智能，精准预测晶体管多区非线性电流响应。
PRIME: Physics-Related Intelligent Mixture of Experts for Transistor Characteristics Prediction
- 基于物理先验设计混合专家架构，动态选择适配输入特征的专家模型。
- 在多种环绕栅极结构上实现比现有模型高60%-84%的预测准确率提升。
- 适合芯片设计与制造中需高精度建模的场景，尤其擅长复杂非线性特性捕捉。
近年来，机器学习广泛应用于工艺爬坡阶段的数据预测，尤其聚焦于电路设计与制造中的晶体管特性。然而，神经网络在捕捉多个工作区域间的非线性电流响应方面仍面临挑战。为此，本文提出一种新型机器学习框架PRIME（Physics-Related Intelligent Mixture of Experts），以整合复杂区域特性。该框架将物理先验知识与数据驱动智能相结合，通过门控网络中的动态加权机制，根据输入特征自适应激活相应专家模型。在多种环绕栅极（GAA）结构上的大量实验表明，PRIME相较于最先进模型预测精度显著提升60%至84%。
原文摘要 · Abstract (English)
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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