An innovative methodology that leverages artificial intelligence (AI) and graph representation for semiconductor device encoding in TCAD device simulation is proposed. A graph-based universal encoding scheme is presented that not only considers material-level and device-level embeddings, but also introduces a novel spatial relationship embedding inspired by interpolation operations typically used in finite element meshing. Universal physical laws from device simulations are leveraged for comprehensive data-driven modeling, which encompasses surrogate Poisson emulation and current-voltage (IV) prediction based on drift-diffusion model. Both are achieved using a novel graph attention network, referred to as RelGAT. Comprehensive technical details based on the device simulator Sentaurus TCAD are presented, empowering researchers to adopt the proposed AI-driven Electronic Design Automation (EDA) solution at the device level.

翻译：提出了一种创新方法，该方法利用人工智能和图形表示技术，在TCAD器件仿真中实现半导体器件编码。我们提出了一种基于图的通用编码方案，该方案不仅考虑了材料级和器件级嵌入，还引入了一种新颖的空间关系嵌入，其灵感来源于有限元网格划分中常用的插值操作。利用器件仿真中的通用物理规律进行全面的数据驱动建模，包括基于替代泊松仿真和基于漂移-扩散模型的电流-电压（IV）预测。两者均通过一种新颖的图注意力网络（称为RelGAT）实现。基于器件仿真器Sentaurus TCAD的全面技术细节得以呈现，使研究人员能够在器件层面采用所提出的AI驱动的电子设计自动化（EDA）解决方案。