Strong enhancement of light–matter interaction in graphene coupled to a photonic crystal nanocavity. Gan XT, Mak KF, Gao YD, You YM, Hatami F et al. Revisitation of extraordinary young’s interference: from catenary optical fields to spin–orbit interaction in metasurfaces. Nanophotonics: interactions, materials, and applications.

Shen YZ, Friend CS, Jiang Y, Jakubczyk D, Swiatkiewicz J et al. In addition, our code and data are publicly available, which could be used for future inverse design model development and benchmarking. Our findings could serve as a guideline for researchers to select the model that can best suit their design criteria and fabrication considerations. We find that tandem networks and Variational Auto-Encoders give the best accuracy, while Generative Adversarial Networks lead to the most diverse predictions. We provide detailed comparisons in terms of their accuracy, diversity, and robustness. Here, we benchmark three commonly used deep learning models in inverse design: Tandem networks, Variational Auto-Encoders, and Generative Adversarial Networks.

Additionally, the relative strength of different deep learning-based inverse design approaches has not been fully investigated. Although most of these neural network models have demonstrated high accuracy in different inverse design problems, no previous study has examined the potential effects under given constraints in nanomanufacturing. Recently, deep learning-based approaches have been developed to tackle the problem of inverse design efficiently.

However, traditional methods based on optimization algorithms are time-consuming and computationally expensive. Photonic inverse design concerns the problem of finding photonic structures with target optical properties.

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