Revolutionizing Semiconductor Chip Design through Generative AI and Reinforcement Learning: A Novel Approach to Mask Patterning and Resolution Enhancement

Abstract

The semiconductor industry is facing increasing challenges in achieving higher performance and efficiency as transistor sizes shrink and fabrication processes become more complex. Traditional chip design methods struggle to keep pace with these advancements, creating a need for innovative approaches. This paper explores a novel methodology for revolutionizing semiconductor chip design by leveraging Generative AI and Reinforcement Learning (RL). The proposed approach focuses on two key areas: mask patterning and resolution enhancement. By utilizing Generative AI, the model autonomously generates optimized mask layouts, which are crucial for defining patterns during lithography. Reinforcement Learning is then employed to iteratively refine and improve the designs, optimizing resolution and minimizing defects in the final chip fabrication. This integration of AI-driven design tools accelerates the development cycle, reduces errors, and enhances the overall yield, offering a significant breakthrough in the semiconductor manufacturing process. The results demonstrate that this innovative method not only improves the accuracy of mask patterning but also achieves higher resolution, providing a pathway for more efficient and scalable semiconductor production in the age of advanced technology.