Timing analysis with compact variation-aware standard cell models
Integration, the VLSI Journal
Fast and accurate statistical criticality computation under process variations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Dominant critical gate identification for power and yield optimization in logic circuits
Proceedings of the 20th symposium on Great lakes symposium on VLSI
Testability driven statistical path selection
Proceedings of the 48th Design Automation Conference
On the computation of criticality in statistical timing analysis
Proceedings of the International Conference on Computer-Aided Design
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The large-scale process and environmental variations for today's nanoscale ICs require statistical approaches for timing analysis and optimization. In this paper, we demonstrate why the traditional concept of slack and critical path becomes ineffective under large-scale variations and propose a novel sensitivity framework to assess the ldquocriticalityrdquo of every path, arc, and node in a statistical timing graph. We theoretically prove that the path sensitivity is exactly equal to the probability that a path is critical and that the arc (or node) sensitivity is exactly equal to the probability that an arc (or a node) sits on the critical path. An efficient algorithm with incremental analysis capability is developed for fast sensitivity computation that has linear runtime complexity in circuit size. The efficacy of the proposed sensitivity analysis is demonstrated on both standard benchmark circuits and large industrial examples.