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.

使用人工智能和图形表示的创新方法，提出了一种在 TCAD 器件模拟中用于半导体器件编码的技术。提出了一个基于图形的通用编码方案，不仅考虑了材料级和器件级嵌入，还引入了一种新型的空间关系嵌入，灵感来自于有限元网格化中通常使用的插值运算。利用器件模拟中的通用物理定律进行综合的数据驱动建模，包括基于漂移扩散模型的拟合泊松仿真和电流电压（IV）预测。两者都采用了一种称为 RelGAT 的新型图形注意力网络。给出了基于设备模拟器 Sentaurus TCAD 的全面技术细节，使研究人员能够在设备级别采用提出的基于人工智能的电子设计自动化（EDA）解决方案。