Design of multilayer optical thin-films based on light scattering properties and using deep neural networks

Hybrid supervised and reinforcement learning for the design and optimization of nanophotonic structures

From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components. However, both data-driven and exploration-based machine learning strategies have limitations in their effectiveness for nanophotonic inverse design. Supervised machine learning approaches require large quantities of training data to produce high-performance models and have difficulty generalizing beyond training data given the complexity of the design space. Unsupervised and reinforcement learning-based approaches on the other hand can have very lengthy training or optimization times associated with them. Here we demonstrate a hybrid supervised learning and reinforcement learning approach to the inverse design of nanophotonic structures and show this approach can reduce training data dependence, improve the generalizability of model predictions, and significantly shorten exploratory training times. The presented strategy thus addresses several contemporary deep learning-based challenges, while opening the door for new design methodologies that leverage multiple classes of machine learning algorithms to produce more effective and practical solutions for photonic design.

Far ultraviolet mirrors for aurora imaging: design and fabrication

The emission lines of 140-180 nm are auroral bands of N ₂ Lyman-Birge-Hopfield, and they have been imaging targets of many satellites that need reflective mirrors. To obtain good imaging quality, the mirrors also should have excellent out-of-band reflection suppression as well as high reflectance at working wavelengths. We designed and fabricated non-periodic multilayer L a F ₃/M g F ₂ mirrors with working wave bands of 140-160 nm and 160-180 nm, respectively. We used a match design method and deep search method to design the multilayer. Our work has been utilized in the new wide-field auroral imager of China, and the application of these notch mirrors with excellent out-of-band suppression reduces the utilization of corresponding transmissive filters in the optical system of space payload. Furthermore, our work provides new routes for the design of other reflective mirrors in the far ultraviolet region.

TMM-Fast, a transfer matrix computation package for multilayer thin-film optimization: tutorial

Achieving the desired optical response from a multilayer thin-film structure over a broad range of wavelengths and angles of incidence can be challenging. An advanced thin-film structure can consist of multiple materials with different thicknesses and numerous layers. Design and optimization of complex thin-film structures with multiple variables is a computationally heavy problem that is still under active research. To enable fast and easy experimentation with new optimization techniques, we propose the Python package Transfer Matrix Method - Fast (TMM-Fast), which enables parallelized computation of reflection and transmission of light at different angles of incidence and wavelengths through the multilayer thin film. By decreasing computational time, generating datasets for machine learning becomes feasible, and evolutionary optimization can be used effectively. Additionally, the subpackage TMM-Torch allows us to directly compute analytical gradients for local optimization by using PyTorch Autograd functionality. Finally, an OpenAI Gym environment is presented, which allows the user to train new reinforcement learning agents on the problem of finding multilayer thin-film configurations.

Machine-learning reinforcement for optimizing multilayered thin films: applications in designing broadband antireflection coatings

The design and fabrication of nanoscale multilayered thin films play an essential role in regulating the operation efficiency of sensitive optical sensors and filters. In this paper, we introduce a packaged tool that employs flexible electromagnetic calculation software with machine learning in order to find the optimized double-band antireflection coatings in intervals of wavelength from 3 to 5 µm and 8 to 12 µm. Instead of computing or modeling an extremely enormous set of thin film structures, this tool enhanced with machine learning can swiftly predict the optical properties of a given structure with >99.7% accuracy and a substantial reduction in computation costs. Furthermore, the tool includes two learning methods that can infer a global optimal structure or suitable local optimal ones. Specifically, these well-trained models provide the highest accurate double-band average transmission coefficient combined with the lowest number of layers or the thinnest total thickness starting from a reference multilayered structure. Finally, the more sophisticated enhancement method, called the double deep Q-learning network, exhibited the best performance in finding optimal antireflective multilayered structures with the highest double-band average transmission coefficient of about 98.95%.