CorDBA: Corners Decoupled Bayesian Approach for yield optimization

Yield optimization is ubiquitous in circuit design but remains elusive for advanced nodes. This is largely due to the dilemma between one's limited resources and the mounting cost of transistor-level simulation in yield estimation. To address this challenge, we propose a novel framework (CorDBA) to optimize yield using the statistical corners of process variations. By introducing a novel corner extraction method, our approach decouples the yield optimization process from its expensive yield estimation. With the help of max-minimum optimization, efficient computation is then conducted on the statistical quantiles of circuit performance metrics. In addition, CorDBA enables sequential yield optimization at multiple stages, in which information from lower yield may be utilized in latter optimization of higher yield. We examine its effectiveness via experiments including analog and digital circuit, as well as circuit with emerging device. We found that CorDBA provides 3.6x speedup (in terms of SPICE simulations) over the state-of-the-art method with 134.7x improvements in robustness (in terms of standard deviation).