Optimization Method for Integrated Circuit Design Based on Artificial Intelligence

Abstract

The current integrated circuit design optimization faces challenges such as high design complexity, diverse optimization objectives,
and low optimization efficiency. This article analyzes the key issues in integrated circuit design optimization and proposes an artificial intelligence based design optimization method. The method clarifies the optimization objectives through requirement analysis, intelligently extracts
design features using machine learning models, and then models and solves the optimization problem to evaluate the optimization effect.
This method can automatically and intelligently assist designers in exploring the design space, and has significant effects in reducing design
complexity, balancing multi-objective optimization, and improving optimization efficiency, providing new ideas for integrated circuit design
optimization in the new era.