Inverse-designed phase prediction in digital lasers using deep learning and transfer learning

Key Points

• Accurate phase prediction enhances the performance of digital lasers, leading to better efficiency and functionality.
• The model shows a 95% accuracy rate in predicting phase compared to traditional methods, providing significant improvements in design.
• Assessment using deep learning and transfer learning techniques demonstrates their effectiveness in optimizing laser configurations.
• Highlights the potential of machine learning to revolutionize the design of digital lasers for various applications.