SourceNet: CNN-based source prediction for parametric source mask optimization

Background: Source mask optimization (SMO) is a crucial technique in photolithography, particularly for sub-resolution printing. It enhances the ability to produce complex patterns and significantly increases the process window, often by a factor of 2× to 6×. Traditional SMO methods, however, can be computationally intensive and time-consuming. Aim: This research aims to introduce a machine learning (ML)-based method for enhancing SMO by predicting seed sources using a convolutional neural network (CNN), thereby improving efficiency and integration in photolithography processes. Approach: The proposed method leverages a CNN within parametric SMO simulations to optimize source patterns. This CNN-based approach processes multiple clip inputs essential for source optimization while maintaining independence from aerial image parameters, thereby improving the adaptability and robustness of the optimization process. Results: The experimental implementation of the CNN model resulted in a significant 87.5% increase in speed compared with conventional optimization techniques. Furthermore, the method was seamlessly integrated into existing SMO workflows. Conclusion: The study highlights the significant potential of applying ML, especially CNNs, to the SMO process. This approach leads to faster optimization, improved print fidelity, and a wider process window, making it a valuable addition to advanced photolithography techniques.