AI-Powered VLSI Design Automation for Faster Chip Production in Emerging Technologies

The optimization techniques used in conventional VLSI design automation are rule-based and limited, which results in ineffective process execution and mistake detection. By using machine learning (ML) methods like Random Forest Regression (RFR), Convolutional Neural Networks (CNN), and Reinforcement Learning (RL), AI-powered VLSI design automation overcomes these constraints. Traditional design techniques are unable to keep up with next-generation semiconductor technology, which calls for AI-driven methods. The proposed approach increases accuracy and efficiency while decreasing human participation by automating many design processes. Experimental findings show a 20-30% reduction in power usage and a 50% reduction in design time (e.g., 75 hours vs. 120 hours for high-complexity designs) as compared to traditional approaches. The approach significantly reduces error rates by more than 60% and increases answer generation quality, attaining 89% accuracy for high-complexity designs compared to 75% in previous techniques. These improvements in design accuracy, scalability, and power efficiency represent a major step toward more effective automation of semiconductor design.