Accurate prediction of Jacobian is essential for multi-variable optical proximity correction (OPC). The Jacobian means the small variation of edge placement error (EPE) induced by small mask bias of nearby segments under optical proximity effects. If the Jacobian can be accurately calculated, it is helpful for OPC iteration reduction, or EPE improvement for 2D shape mask patterns. Moreover, if this can be cost-effective, this approach can be easily extended to Full chip level. We changed expensive Jacobian matrix procedure into simple ML based Jacobian model inference. Thanks to efficiently chosen geometric and optical features and light ANN structure, our method can predict Jacobian 76% faster and 81% more accurate than intensity distribution function method. We also improved mask optimization algorithm by inserting small gradient iterations. Our mask optimization solver was 2 times faster than vanilla mask optimization solver. Through this effort, we constructed fast and accurate machine learning assisted mask optimization solver.
As the physical design of semiconductors continues to shrink, the lithography process is becoming more sensitive to layout design. Identifying lithography hotspots (HSs) in the layout design stage appears to be more and more crucial for fast semiconductor development. In this direction, we propose an accurate HS detection method using convolutional neural networks. Our approach produces more accurate detection performance (95.5% recall and 22.2% precision) compared to previous approaches. In spite of the use of deep convolutional neural networks, our method achieves a fast detection time of 0.72 h/mm2. In order to quickly and accurately detect HSs, we not only utilize the nature of convolutional-neural networks but also make additional technical efforts to improve the performance of our framework, including inspection region reduction, data augmentation, DBSCAN clustering, modified batch normalization, and fast image scanning. To the best of our knowledge, our approach is the first CNN-based lithography HS detection.
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