Crosstalk Prediction in Integrated Circuits Based on Machine Learning Techniques

EasyChair Preprint 11049

Abstract

Unintentional signal coupling between adjacent wires known as crosstalk is a common problem in integrated circuits (IC) and became major with operating frequencies rise and circuit dimensions decrease. Performance decline, signal distortion, and functional failures could all result from this phenomenon. Hence, having reliable crosstalk prediction and reduction mechanisms is a crucial aspect of IC design. Machine learning (ML) is currently a widely utilized technique in prediction algorithms. The suggested approach combines crosstalk analysis and ML to explore ways to predict crosstalk and reduce disturbances in ICs taking as input the physical design of IC. Training data for the ML model is collected from the parsing algorithm of IC information. Experiments are done for different types of designs (standard cells, memories, etc.). As a result, the trained ML model provides approximately 90% pass rate.

Keyphrases: Crosstalk, Signal Integrity, crosstalk prediction, deep learning, machine learning, neural network

@booklet{EasyChair:11049,
  author    = {Gor Abgaryan},
  title     = {Crosstalk Prediction in Integrated Circuits Based on Machine Learning Techniques},
  howpublished = {EasyChair Preprint 11049},
  year      = {EasyChair, 2023}}