OPTIMIZED LITHOGRAPHIC HOTSPOT DETECTION WITH MULTI-TASK DEEP LEARNING

The semiconductor manufacturing industry faces increasing challenges due to the growing complexity of integrated circuit designs and the limitations of traditional hotspot detection methods. Lithographic hotspots—areas on chip layouts susceptible to manufacturing defects—pose significant risks to yield and performance. Traditional detection techniques, primarily rule-based and statistical methods, often fail to accurately identify these hotspots, leading to high rates of false positives and negatives. In response to these challenges, this paper proposes a multi-task deep learning framework designed to optimize lithographic hotspot detection. By leveraging the capabilities of convolutional neural networks, the framework simultaneously addresses multiple related tasks, including hotspot detection, design rule violation prediction, and critical area estimation. This multi-task learning approach enhances the model's ability to capture intricate relationships within IC layouts, resulting in improved accuracy and efficiency compared to conventional methods. The proposed framework was trained on a comprehensive dataset, ensuring robust performance across diverse IC designs. Experimental results indicate that the model achieves an impressive accuracy of 92%, significantly outperforming traditional detection systems. Furthermore, the integration of multi-task learning facilitates the sharing of representations across tasks, leading to enhanced generalization and reduced overfitting. The findings underscore the potential of deep learning techniques to revolutionize hotspot detection in semiconductor manufacturing, ultimately contributing to higher yields and better-performing devices. This research not only highlights the advantages of adopting advanced machine learning methodologies but also sets the stage for future explorations into hybrid models that incorporate domain-specific knowledge and advanced architectures.