Abstract

Process variation has become a significant concern for static timing analysis. In this paper, we present a new method for path-based statistical timing analysis. We first propose a method for modeling inter-and intra-die device length variations. Based on this model, we then present an efficient method for computing the total path delay probability distribution using a combination of device length enumeration for inter-die variation and an analytical approach for intra-die variation. We also propose a simple and effective model of spatial correlation of intra-die device length variation. The analysis is then extended to include spatial correlation. We test the proposed methods on paths from an industrial high-performance microprocessor and present comparisons with traditional path analysis which does not distinguish between inter-and intradie variations. The characteristics of the device length distributions were obtained from measured data of 8 test chips with a total of 17688 device length measurements. Spatial correlation data was also obtained from these measurements. We demonstrate the accuracy of the proposed approach by comparing our results with Monte-Carlo simulation.

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In this paper, we propose a new sensitivity based, statistical gate sizing method. Since circuit optimization effects the entire shape of the circuit delay distribution, it is difficult to capture the quality of a distribution with a single metric. Hence, we first introduce a new objective function that provides an effective measure for the quality of a delay distribution for both ASIC and high performance designs. We then propose an efficient and exact sensitivity based pruning algorithm based on a newly proposed theory of perturbation bounds. A heuristic approach for sensitivity computation which relies on efficient computation of statistical slack is then introduced. Finally, we show how the pruning and statistical slack based approaches can be combined to obtain nearly identical results compared with the brute-force approach but with an average run-time improvement of up to 89x. We also compare the optimization results against that of a deterministic optimizer and show an improvement up to 16% in the 99-percentile circuit delay and up to 31% in the standard deviation for the same circuit area.