Sat 06.01.19
Sat 06.01.19
Sat 06.01.19
Sat 06.01.19
Artificial intelligence especially deep learning has enabled many breakthroughs in both academia and industry. This project aims to create a generative and versatile design approach based on novel deep learning techniques to realize integrated, multi-functional photonic systems, and provide proof-of-principle demonstrations in experiments. Compared with traditional approaches using extensive numerical simulations or inverse design algorithms, deep learning can uncover the highly complicated relationship between a photonic structure and its properties from the dataset, and hence substantially accelerate the design of novel photonic devices that simultaneously encode distinct functionalities in response to the designated wavelength, polarization, angle of incidence and other parameters. Such multi-functional photonic systems have important applications in many areas, including optical imaging, holographic display, biomedical sensing, and consumer photonics with high efficiency and fidelity, to benefit the public and the nation.
The integrated education plan will considerably enhance outreach activities and educate students in grades 7-12, empowered by the successful experience and partnership previously established by the PIs. Graduate and undergraduate students participating in the project will learn the latest developments in the multidisciplinary fields of photonics, deep learning and advanced manufacturing, and gain real-world knowledge by engaging industrial collaborators in tandem with Northeastern University’s renowned cooperative education program.
Artificial intelligence especially deep learning has enabled many breakthroughs in both academia and industry. This project aims to create a generative and versatile design approach based on novel deep learning techniques to realize integrated, multi-functional photonic systems, and provide proof-of-principle demonstrations in experiments. Compared with traditional approaches using extensive numerical simulations or inverse design algorithms, deep learning can uncover the highly complicated relationship between a photonic structure and its properties from the dataset, and hence substantially accelerate the design of novel photonic devices that simultaneously encode distinct functionalities in response to the designated wavelength, polarization, angle of incidence and other parameters. Such multi-functional photonic systems have important applications in many areas, including optical imaging, holographic display, biomedical sensing, and consumer photonics with high efficiency and fidelity, to benefit the public and the nation.
The integrated education plan will considerably enhance outreach activities and educate students in grades 7-12, empowered by the successful experience and partnership previously established by the PIs. Graduate and undergraduate students participating in the project will learn the latest developments in the multidisciplinary fields of photonics, deep learning and advanced manufacturing, and gain real-world knowledge by engaging industrial collaborators in tandem with Northeastern University’s renowned cooperative education program.
