Photonics inverse design relies on human experts to search for a design topology that satisfies certain optical specifications with their experience and intuitions, which is relatively labor‐intensive, slow, and sub‐optimal. Machine learning has emerged as a powerful tool to automate this inverse design process. However, supervised or semi‐supervised deep learning is unsuitable for this task due to: (1) a severe shortage of available training data due to the high computational complexity of physics‐based simulations along with a lack of open‐source datasets and/or the need for a pre‐trained neural network model; (2) the issue of one‐to‐many mapping or non‐unique solutions; and (3) the inability to perform optimization of the photonic structure beyond inverse designing. Reinforcement Learning (RL) has the potential to overcome the above three challenges. Here, we propose Learning to Design Optical‐Resonators (L2DO) to leverage RL that learns to autonomously inverse design nanophotonic laser cavities without any prior knowledge while retrieving unique design solutions. L2DO incorporates two different algorithms – Deep Q‐learning and Proximal Policy Optimization. We evaluate L2DO on two laser cavities: a long photonic crystal (PC) nanobeam and a PC nanobeam with an L3 cavity, both popular structures for semiconductor lasers. Trained for less than 152 hours on limited hardware resources, L2DO has improved state‐of‐the‐art results in the literature by over 2 orders of magnitude and obtained 10 times better performance than a human expert working the same task for over a month. L2DO first learned to meet the required maxima of Q ‐factors (>50 million) and then proceeded to optimize some additional good‐to‐have features (e.g., resonance frequency, modal volume). Compared with iterative human designs and inverse design via supervised learning, L2DO can achieve over two orders of magnitude higher sample‐efficiency without suffering from the three issues above. This work confirms the potential of deep RL algorithms to surpass human designs and marks a solid step towards a fully automated AI framework for photonics inverse design.

A deep learning (DL)-based approach has been proposed to accurately model the relationship between design parameters and the Q factor of photonic crystal (PC) nanocavities. A convolutional neural network (CNN), which consists of two convolutional layers and three fully-connected layers is trained on a large-scale dataset consisting of 12,500 nanocavities. The experimental results show that the CNN is able to achieve a state-of-the-art performance in terms of prediction accuracy (i.e., up to 99.9999%) and convergence speed (i.e., orders-of-magnitude speedup). The proposed approach overcomes shortcomings of existing methods and paves the way for DL-based on-demand and data-driven optimization of PC nanocavities applicable to the rapid design of nanoscale lasers and photonic integrated circuits. We will open source the database and code as one of our main contributions to the photonics research community.

The design of nanophotonic structures based on deep learning is emerging rapidly in the research community. Design methods using Deep Neural Networks (DNN) are outperforming conventional physics-based simulations performed iteratively by human experts. Here, a self-adaptive and regularized DNN based on Convolutional Neural Networks (CNNs) for the smart and fast characterization of nanophotonic structures in high-dimensional design parameter space is presented. This proposed CNN model, named LRS-RCNN, utilizes dynamic learning rate scheduling and L2 regularization techniques to overcome overfitting and speed up training convergence and is shown to surpass the performance of all previous algorithms, with the exception of two metrics where it achieves a comparable level relative to prior works. We applied the model to two challenging types of photonic structures: 2D photonic crystals (e.g., L3 nanocavity) and 1D photonic crystals (e.g., nanobeam) and results show that LRS-RCNN achieves record-high prediction accuracies, strong generalizibility, and substantially faster convergence speed compared to prior works. Although still a proof-of-concept model, the proposed smart LRS-RCNN has been proven to greatly accelerate the design of photonic crystal structures as a state-of-the-art predictor for both Q-factor and V. It can also be modified and generalized to predict any type of optical properties for designing a wide range of different nanophotonic structures. The complete dataset and code will be released to aid the development of related research endeavors.

We study a new technique for solving the fundamental challenge in nanophotonic design: fast and accurate characterization of nanoscale photonic devices with minimal human intervention. Much like the fusion between Artificial Intelligence and Electronic Design Automation (EDA), many efforts have been made to apply deep neural networks (DNN) such as convolutional neural networks to prototype and characterize next-gen optoelectronic devices commonly found in Photonic Integrated Circuits. However, state-of-the-art DNN models are still far from being directly applicable in the real world: e.g., DNN-produced correlation coefficients between target and predicted physical quantities are about 80%, which is much lower than what it takes to generate reliable and reproducible nanophotonic designs. Recently, attention-based transformer models have attracted extensive interests and been widely used in Computer Vision and Natural Language Processing. In this work, we for the first time propose a Transformer model (POViT) to efficiently design and simulate photonic crystal nanocavities with multiple objectives under consideration. Unlike the standard Vision Transformer, our model takes photonic crystals as input data and changes the activation layer from GELU to an absolute-value function. Extensive experiments show that POViT significantly improves results reported by previous models: correlation coefficients are increased by over $12\%$ (i.e., to $92.0\%$) and prediction errors are reduced by an order of magnitude, among several key metric improvements. Our work has the potential to drive the expansion of EDA to fully automated photonic design (i.e., PDA). The complete dataset and code will be released to promote research in the interdisciplinary field of materials science/physics and computer science.

Optical resonant cavities with high quality factor (Q-factor) are widely used in science and technology for their capabilities of strong confinement of light and enhanced light–matter interaction. The 2D photonic crystal structure with bound states in the continuum (BICs) is a novel concept for resonators with ultra-compact device size, which can be used to generate surface emitting vortex beams based on symmetry-protected BICs at the Γ point. Here, to the best of our knowledge, we demonstrate the first photonic crystal surface emitter with a vortex beam by using BICs monolithically grown on CMOS-compatible silicon substrate. The fabricated quantum-dot BICs-based surface emitter operates at 1.3 µm under room temperature (RT) with a low continuous wave (CW) optically pumped condition. We also reveal the BIC's amplified spontaneous emission with the property of a polarization vortex beam, which is promising to provide a novel degree of freedom in classical and quantum realms.