Due to the significant process variations, designers have to optimize the statistical performance distribution of nano-scale IC design in most cases. This problem has been investigated for decades under the formulation of stochastic optimization, which minimizes the expected value of a performance metric while assuming that the distribution of process variation is exactly given. This paper rethinks the variation-aware circuit design optimization from a new perspective. First, we discuss the variation shift problem, which means that the actual density function of process variations almost always differs from the given model and is often unknown. Consequently, we propose to formulate the variation-aware circuit design optimization as a distributionally robust optimization problem, which does not require the exact distribution of process variations. By selecting an appropriate uncertainty set for the probability density function of process variations, we solve the shift-aware circuit optimization problem using distributionally robust Bayesian optimization. This method is validated with both a photonic IC and an electronics IC. Our optimized circuits show excellent robustness against variation shifts: the optimized circuit has excellent performance under many possible distributions of process variations that differ from the given statistical model. This work has the potential to enable a new research direction and inspire subsequent research at different levels of the EDA flow under the setting of variation shift.

翻译：由于显著的工艺变化，设计者在大多数情况下必须优化纳米级集成电路设计的统计性能分布。这一问题在随机优化的框架下已研究数十年，其目标是假设工艺变化的分布精确已知时，最小化某一性能指标的期望值。本文从新的视角重新审视了考虑变异性的电路设计优化。首先，我们讨论了变异性偏移问题，即工艺变化的实际密度函数几乎总是与给定模型存在差异且通常未知。因此，我们提出将考虑变异性的电路设计优化建模为分布鲁棒性优化问题，该问题无需确切的工艺变化分布信息。通过为工艺变化的概率密度函数选择合适的不确定集，我们采用分布鲁棒性贝叶斯优化解决了考虑偏移的电路优化问题。该方法在光子集成电路与电子集成电路上均得到了验证。优化后的电路在变异性偏移下展现出卓越的鲁棒性：当工艺变化的分布与给定统计模型存在差异时，优化电路仍能保持优异性能。本研究有望开启新的研究方向，并启发电子设计自动化流程中不同层级在变异性偏移场景下的后续研究。