Interdisciplinary analysis and optimization of digital photonic devices for meta-photonics
Xiaohua Xing,
Yuqi Ren,
Die Zou,
Qiankun Zhang,
Bingxuan Mao,
Jianquan Yao,
Deyi Xiong,
Liang Wu
Affiliations
Xiaohua Xing
College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Key Laboratory of Optoelectronics Information and Technology (Ministry of Education), Tianjin 300072, China
Yuqi Ren
College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
Die Zou
College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Key Laboratory of Optoelectronics Information and Technology (Ministry of Education), Tianjin 300072, China
Qiankun Zhang
College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Key Laboratory of Optoelectronics Information and Technology (Ministry of Education), Tianjin 300072, China
Bingxuan Mao
College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Key Laboratory of Optoelectronics Information and Technology (Ministry of Education), Tianjin 300072, China
Jianquan Yao
College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Key Laboratory of Optoelectronics Information and Technology (Ministry of Education), Tianjin 300072, China
Deyi Xiong
College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
Liang Wu
College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Key Laboratory of Optoelectronics Information and Technology (Ministry of Education), Tianjin 300072, China; Corresponding author
Summary: 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.
