A DEEP LEARNING APPROACH TO LITHOGRAPHIC HOTSPOT DETECTION IN SEMICONDUCTOR MANUFACTURING

This paper presents a novel deep learning approach to lithographic hotspot detection in semiconductor manufacturing, addressing the critical challenges posed by increasingly complex integrated circuit designs. As the demand for smaller, faster, and more efficient semiconductor devices continues to rise, the intricacies of the lithography process become more pronounced, leading to potential defects that can significantly impact yield and performance. Traditional hotspot detection methods, which primarily rely on rule-based and statistical techniques, often fall short in capturing the complexities of modern IC layouts, resulting in missed hotspots and compromised product quality. In contrast, this research leverages advanced machine learning techniques, specifically convolutional neural networks, to enhance the accuracy and reliability of hotspot detection. By training the model on both simulated and real-world datasets, the proposed framework demonstrates superior performance in identifying hotspots while minimizing false positives. The findings highlight the advantages of deep learning over conventional methods, showcasing its ability to learn intricate patterns and relationships within design data. This research not only contributes to the advancement of hotspot detection methodologies but also lays the groundwork for future applications of deep learning in various areas of semiconductor manufacturing. The implications of this study are far-reaching, as improved hotspot detection can lead to higher yields, better-performing devices, and ultimately, a more efficient semiconductor manufacturing process.