Routing-Free Crosstalk Prediction

Interconnect spacing is getting increasingly smaller in advanced technology nodes, which adversely increases the capacitive coupling of adjacent interconnect wires. It makes crosstalk a significant contributor to signal integrity and timing, and it is now imperative to prevent crosstalk-induced noise and delay issues in the earlier stages of VLSI design flow. Nonetheless, since the crosstalk effect depends primarily on the switching of neighboring nets, accurate crosstalk evaluation is only viable at the late stages of design flow with routing information available, e.g., after detailed routing. There have also been previous efforts in early-stage crosstalk prediction, but they mostly rely on time-expensive trial routing. In this work, we propose a machine learning-based routing-free crosstalk prediction framework. Given a placement, we identify routing and net topology-related features, along with electrical and logical features, which affect crosstalk-induced noise and delay. We then employ machine learning techniques to train the crosstalk prediction models, which can be used to identify crosstalk-critical nets in placement stages. Experimental results demonstrate that the proposed method can instantly classify more than 70% of crosstalk-critical nets after placement with a false-positive rate of less than 2%.