On-Chip Training of Crosstalk Predictors to Fit Uncertainties

Abstract

Crosstalk noise has been strongly threatened the signal integrity of interconnects in new sub-micrometer technology nodes. The crosstalk prediction helps to avoid crosstalk consequences. Static crosstalk models cannot predict crosstalk faults intensified by thermal, fab-induced, and temporal uncertainties. The goal of the paper is to avoid crosstalk by the use of a dynamic crosstalk predictor which can adapt itself in the presence of uncertainties. To begin with, the neural network model of the crosstalk phenomenon would be extracted based on data transition of communication wires. This model is implemented as an on-chip crosstalk predictor. In addition, this predictor would be trained on-chip by hardware implementing the learning algorithm. Simulation results show that the proposed predictor is much more tolerant of uncertainties than the static predictors.