FET/SHF: Small: Reinforcement learning and transformer inspired smart photonics inverse design

The field of optics and photonics has transformed our lives with such modern technologies as optical communications, solar energy sources, space telescopes and high-resolution photolithography. For a long time, human experts had to perform optical design manually relying on their accumulated experience and physical intuitions. This is especially true for photonic inverse design, where one must find the appropriate photonic structures with the desired optical functions. Recently, deep learning approaches have been pursued by scientific community to automatically design sophisticated structures that can satisfy the design objective, leading to substantial progress in this direction. This research project will enable photonics non-experts to use the developed Artificial Intelligence (AI) model to obtain solutions to their individual optical design problems. Furthermore, understanding the underlying operational principles will advance more generalizable photonics knowledge and enable researchers to develop new structures faster. To accomplish their goals, the research team will explore and apply two powerful AI technologies. The first tool to be studied is deep reinforcement learning, a sequential generation process that learns to design structures with trial-and-reward, in a way to mimic how human and animals learn to interact with the world. Cooperative learning between machine and human will be pursued, where human teaches machine to learn, and machine inspires human to understand. This combined input will benefit the realization of various types of optical structures. The second method utilizes the transformer method, the powerhouse behind the highly successful powerful large language models, for smart optical design. The research team will leverage the Foundation model, the large machine learning models that tackle various downstream tasks once trained on diverse, and large-scale data, to address the optical inverse design of large-scale and complicated nanostructures. The knowledge gained through the study will be applied further to a few testbeds for experimental demonstrations. With more people using the technology and increased data available for training the neural network, one can anticipate its learning and generative capabilities will advance and will the users even more effectively. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.