Application of Machine Learning for Anticipating Chip Failure Rates in VLSI Design

The purpose of this research is to investigate the incorporation of machine literacy techniques for the prediction of chip failure rates in the design of veritably Large Scale Integration (VLSI) systems. The complexity of very large-scale integrated circuits (VLSI) is increasing as semiconductor technology progresses, which presents issues in terms of their ability to be trusted. When it comes to dealing with the complexities of ultramodern chip infrastructures, traditional methods of failure rate prediction typically see a dramatic decline. The purpose of this is to improve the accuracy and precision of failure rate forecasts by analysing a wide range of elements, including design specifications, environmental factors, and manufacturing variables. This will be accomplished through the utilization of machine literacy algorithms. Our method tries to discover patterns and dependencies that contribute to chip failures using extensive data analysis and model training. As a result, it provides suppliers with a valuable instrument that can be used to proactively solve implicit trust ability issues during the VLSI design process. Within the constantly shifting landscape of semiconductor technology, the findings of this investigation present a potentially fruitful path toward the improvement of the reliability of very large-scale integrated circuits (VLSI) and the enhancement of the trustworthiness of electronic systems.