Abstract-Manufacturing process variations lead to circuit timing variability and a corresponding timing yield loss. Traditional corner analysis consists of checking all process corners (combinations of process parameter extremes) to make sure that circuit timing constraints are met at all corners, typically by running static timing analysis (STA) at every corner. This approach is becoming too expensive due to the increase in the number of corners with modern processes. As an alternative, we propose a linear-time approach for STA which covers all process corners in a single pass. Our technique assumes a linear dependence of delays and slews on process parameters and provides estimates of the worst case circuit delay and slew. It exhibits high accuracy in practice, and if the circuit has m gates and n relevant process parameters, the complexity of the algorithm is O(mn).
The lack of good "correlation" between pre-silicon simulated delays and measured delays on silicon (silicon data) has spurred efforts on so-called silicon debug. The identification of speedlimiting paths, or simply speedpaths, in silicon debug is a crucial step, required for both "fixing" failing paths and for accurate learning from silicon data. We propose using characterized, presilicon, variational timing models to identify speedpaths that can best explain the observed delays from silicon measurements. Delays of all logic paths are written as affine functions of process parameters, called hyperplanes, and a branch and bound approach is then applied to find the "best" path combinations. Our method has been tested on a set of ISCAS-89 circuits and the results show that it accurately identifies the speedpaths in most cases, and that this is achieved in a very efficient manner.
Abstract-Manufacturing process variations lead to circuit timing variability and a corresponding timing yield loss. Traditional corner analysis consists of checking all process corners (combinations of process parameter extremes) to make sure that circuit timing constraints are met at all corners, typically by running static timing analysis (STA) at every corner. This approach is becoming too expensive due to the increase in the number of corners with modern processes. As an alternative, we propose a linear-time approach for STA which covers all process corners in a single pass. Our technique assumes a linear dependence of delays and slews on process parameters and provides estimates of the worst case circuit delay and slew. It exhibits high accuracy in practice, and if the circuit has m gates and n relevant process parameters, the complexity of the algorithm is O(mn).
In order for the results of timing analysis to be useful, they must provide insight and guidance on how the circuit may be improved so as to fix any reported timing problems. A limitation of many recent variability-aware timing analysis techniques is that, while they report delay distributions, or verify multiple corners, they do not provide the required guidance for re-design. We propose an efficient block-based parameterized timing analysis technique that can accurately capture circuit delay at every point in the parameter space, by reporting all paths that can become critical. Using an efficient pruning algorithm, only those potentially critical paths are carried forward, while all other paths are discarded during propagation. This allows one to examine local robustness to parameters in different regions of the parameter space, not by considering differential sensitivity at a point (which would be useless in this context) but by knowledge of the paths that can become critical at nearby points in parameter space. We give a formal definition of this problem and propose a technique for solving it that improves on the state of the art, both in terms of theoretical computational complexity and in terms of run time on various test circuits.
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