Construction of a Digital Integrated Circuit Backend Design System Based on Machine Learning

With the increasing complexity of integrated circuit (IC) design, traditional backend design methods have become increasingly difficult to meet the requirements, and there is an urgent need for new technological means to improve system design efficiency and reduce system errors. This article can be based on machine learning to reconstruct the backend IC design process, and intelligently analyze and identify it, in order to optimize the layout and wiring. By predicting and optimizing the layout, signal transmission delay and power consumption can be effectively reduced, thereby improving the overall performance of the IC. In cross technology node testing, from the perspective of failure rate, as the number of technology nodes decreases, the failure rate gradually decreases, ranging from 1.5% to 55%. The research results of this article can greatly improve the design accuracy and processing speed of chips, while also greatly reducing the power consumption and cost of chips, providing a new approach for IC design.