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：本文提出了一种创新方法论，通过人工智能（AI）与图表示技术实现半导体器件的TCAD仿真编码。我们设计了基于图的通用编码方案，该方案不仅包含材料级与器件级嵌入表征，还引入了受有限元网格插值操作启发的空间关系嵌入。基于器件仿真的通用物理规律，我们构建了包含替代泊松仿真与基于漂移扩散模型的电流-电压（IV）预测的综合数据驱动建模框架。上述目标均通过名为RelGAT的新型图注意力网络实现。本文还提供了基于Sentaurus TCAD器件仿真器的完整技术细节，为研究人员采用所提出的AI驱动型器件级电子设计自动化（EDA）解决方案提供了有力支撑。