Predicting Chip Failure Rates with Machine Learning in VLSI Design

This exploration paper explores the integration of machineliteracyways toprognosticate chip failure rates in veritably Large Scale Integration(VLSI) design. As semiconductor technology advances, the complexity of VLSI circuits grows, posing challenges inicing theirtrust ability. Traditionalstyles forprognosticating failure ratesfrequently fallsuddenly in handling thecomplications ofultramodern chipinfrastructures. using machineliteracy algorithms, this aims to enhance thedelicacy andeffectiveness of failure rateprognostications byassaying a multitude of parameters, including design specifications, environmental factors, and manufacturing variables. Throughexpansive data analysis and model training, our approach seeks to uncover patterns and dependencesthat contribute to chip failures, therebyfurnishing aprecious tool for contrivers to proactively address implicittrust ability issues during the VLSI design phase. The findings of thisexploration offer a promising avenue forperfecting the robustness of VLSI circuits and advancing thetrust ability of electronic systems in the ever- evolvinggeography of semiconductor technology