Space Mapped Neuromodeling for Fast & Accurate Signal Integrity Analysis of Rough On-chip Copper Interconnects

In this paper, an accurate modeling of on-chip copper interconnects with surface roughness is performed considering the parametric variability. This modeling is highly accurate as per-unit-length parameters of the on-chip rough copper interconnects are extracted via full wave EM solver. Further, a space-mapped artificial neural network (ANN) is developed for accurate prediction of eye height and eye width from the geometrical and material parameters of the rough copper interconnects. The novel space-mapping ANN developed in this work is more efficient in terms of accuracy and requires fewer training samples when compared to conventional ANNs.