This paper proposes a fast system technology co-optimization (STCO) framework that optimizes power, performance, and area (PPA) for next-generation IC design, addressing the challenges and opportunities presented by novel materials and device architectures. We focus on accelerating the technology level of STCO using AI techniques, by employing graph neural network (GNN)-based approaches for both TCAD simulation and cell library characterization, which are interconnected through a unified compact model, collectively achieving over a 100X speedup over traditional methods. These advancements enable comprehensive STCO iterations with runtime speedups ranging from 1.9X to 14.1X and supports both emerging and traditional technologies.

翻译：本文提出了一种快速系统技术协同优化（STCO）框架，用于优化下一代集成电路设计的功耗、性能与面积（PPA），以应对新型材料和器件架构带来的挑战与机遇。我们聚焦于利用人工智能技术加速STCO的技术层面，通过采用基于图神经网络（GNN）的方法分别实现TCAD仿真和单元库表征，二者通过统一的紧凑模型相互连接，相较于传统方法实现了超过100倍的加速。这些进展使得全面的STCO迭代得以实现，运行时间加速比达到1.9倍至14.1倍，并同时支持新兴技术与传统技术。