Towards true crosstalk noise analysis
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
False-noise analysis using logic implications
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Temporofunctional crosstalk noise analysis
Proceedings of the 40th annual Design Automation Conference
False-Noise Analysis using Resolution Method
ISQED '02 Proceedings of the 3rd International Symposium on Quality Electronic Design
Delay noise pessimism reduction by logic correlations
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Pessimism reduction in crosstalk noise aware STA
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
False coupling exploration in timing analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Feasible aggressor-set identification under constraints for maximum coupling noise
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Timing Arc Based Logic Analysis for false noise reduction
Proceedings of the 2009 International Conference on Computer-Aided Design
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Crosstalk noise becomes one of the critical issues gating design closure for nano-meter designs. Pessimism in noise analysis can lead to significant additional time spent addressing false violations. Taking logic correlation into consideration, noise analysis can reduce pessimism significantly by eliminating false noise signals [1]-[3][5]-[7][10]-[13]. Eliminating the aggressors from the aggressor candidate set that can not switch simultaneously restricted by the logic exclusivity (LE) relationship among them can save simulation time as well. The LE problem, being proved as NP-complete, is basically to determine the subset (possibly multiple equivalent subsets) of a given aggressor candidate set which has the largest combined weight out of all possible subsets governed by logic exclusivity constraints. This paper presents a new approach in resolving the LE problem, which employs a gain guided backtrack search technique that does not require exhaustive search of all the binary paths to reach an optimal solution. We first prove that under certain conditions, if the gain at each level is non-negative, then the result will be optimal. Based on this theorem, a new algorithm is developed. The experimental results demonstrate the efficiency and accuracy of this approach. The algorithm can quickly find the optimal solutions for most cases from industry designs and outperforms other methods.