Reduction of Crosstalk Pessimism Using Tendency Graph Approach

Accurate estimation of worst-case crosstalk effects is critical for a realistic estimation of the worst-case behavior of deep sub-micron circuits. Crosstalk analysis models usually assume that the worst-case crosstalk occurs with all the aggressors of a victim (net or path) simultaneously inducing crosstalk even though this may not be possible at all. This overestimated crosstalk is called false noise. Logic correlations have been explored to reduce false noise in J.C. Beck, et al., (2004), which also used branch and bound method to solve the problem. In this paper, we propose a novel approach, named tendency graph approach (TGA), which preprocesses the logic constraints of the circuit to drastically speed up the fundamental branch and bound algorithm. The new approach has been implemented in C++ and tested on an industrial circuit in a current 90 nm technology, demonstrating that TGA considerably accelerates the solution to the false noise problem, and makes in many cases branch and bound feasible in the first place.