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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