fast second-order statistical static timing analysis using parameter dimension reduction
The ability to account for the growing impacts of multiple process variations in modern technologies is becoming an integral part of nanometer VLSI design. Under the context of timing analysis, the need for combating process variations has sparkled a growing body of statistical static timing analysis (SSTA) techniques. While first-order SSTA techniques enjoy good runtime efficiency desired for tackling large industrial designs, more accurate second-order SSTA techniques have been proposed to improve the analysis accuracy, but at the cost of high computational complexity. Although many sources of variations may impact the circuit performance, considering a large number of inter-die and intra-die variations in the traditional SSTA analysis is very challenging. In this paper, we address the analysis complexity brought by high parameter dimensionality in static timing analysis and propose an accurate yet fast second-order SSTA algorithm based upon novel parameter dimension reduction. By developing reduced-rank regression based parameter reduction algorithms within block-based SSTA flow, we demonstrate that accurate second order SSTA analysis can be extended to a much higher parameter dimensionality than what is possible before. Our experimental results have shown that the proposed parameter reduction can achieve up to 10X parameter dimension reduction and lead to significantly improved second-order SSTA analysis under a large set of process variations.
parameter dimension reduction process variation statistical timing