An on-the-fly parameter dimension reduction approach to fast second-order statistical static timing analysis

While first-order statistical static timing analysis (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-and intra-die variations in the traditional SSTA is very challenging. In this paper, we address the analysis complexity brought by high parameter dimensionality in SSTA and propose an accurate yet fast second-order SSTA algorithm based on novel on-the-fly parameter dimension reduction techniques. By developing a reduced rank regression (RRR)-based approach and a method of moments (MOM)-based parameter reduction algorithm within the block-based SSTA flow, we demonstrate that accurate second-order SSTA can be extended to a much higher parameter dimensionality than what is possible before. Our experimental results have shown that the proposed parameter reductions can achieve up to 10 × parameter dimension reduction and lead to significantly improved second-order SSTA under a large set of process variations.