Fast Buffer Insertion for Yield Optimization Under Process Variations

With the emerging process variations in fabrication, the traditional corner-based timing optimization techniques become prohibitive. Buffer insertion is a very useful technique for timing optimization. In this paper, we propose a buffer insertion algorithm with the consideration of process variations. We use the solutions from the deterministic buffering that sets all the random variables at their nominal values to guide the statistical buffering algorithm. Our algorithm keeps the solution lists short, and always achieves higher yield than the deterministic buffering. The experimental results demonstrate that the exiting approaches cannot handle large cases efficiently or effectively, while our algorithm handles large cases very efficiently, and improves the yield more than 12% on average.