  With shrinking interconnect spacing in advanced technology nodes, existing
timing predictions become less precise due to the challenging quantification of
crosstalk-induced delay. During the routing, the crosstalk effect is typically
modeled by predicting coupling capacitance with congestion information.
However, the timing estimation tends to be overly pessimistic, as the
crosstalk-induced delay depends not only on the coupling capacitance but also
on the signal arrival time. This work presents a crosstalk-aware timing
estimation method using a two-step machine learning approach. Interconnects
that are physically adjacent and overlap in signal timing windows are filtered
first. Crosstalk delay is predicted by integrating physical topology and timing
features without relying on post-routing results and the parasitic extraction.
Experimental results show a match rate of over 99% for identifying crosstalk
nets compared to the commercial tool on the OpenCores benchmarks, with
prediction results being more accurate than those of other state-of-the-art
methods.
