Interdisciplinary analysis and optimization of digital photonic devices for meta-photonics

With the continuous integration and development of AI and natural sciences, we have been diligently exploring a computational analysis framework for digital photonic devices. Here, We have overcome the challenge of limited datasets through the use of Generative Adversarial Network networks and transfer learning, providing AI feedback that aligns with human knowledge systems. Furthermore, we have introduced knowledge from disciplines such as image denoising, multi-agent modeling of Physarum polycephalum, percolation theory, wave function collapse algorithms, and others to analyze this new design system. It represents an accomplishment unattainable within the framework of classical photonics theory and significantly improves the performance of the designed devices. Notably, we present theoretical analyses for the drastic changes in device performance and the enhancement of device robustness, which have not been reported in previous research. The proposed concept of meta-photonics transcends the conventional boundaries of disciplinary silos, demonstrating the transformative potential of interdisciplinary fusion.