Optical metasurfaces enable devices to interact with light in unique ways by modulating phase, polarization, or intensity. A metasurface, composed of individual subwavelength scatterers known as meta-atoms, can be designed to provide unparalleled control of wavefronts for a variety of optical applications, yet the design of such devices is often unintuitive and challenging due to computationally expensive forward simulations and the number of free parameters. To overcome this, there is interest in developing inverse design methods as an alternative to conventional forward design. Inverse design leverages machine learning algorithms to effectively search a problem space, starting from application and resulting in solution parameters. In this work, we adopt an inverse design approach that involves targeted forward simulations of arbitrary meta-atoms. To ensure that the dataset captures all possible shapes and rotations of near field responses with second order accuracy, it is constructed using meta-atoms with varying geometries and corresponding phase shifts, including the effect of nearest neighbors. A custom deep learning system is developed to extract meaningful features from this near field response. The proposed framework provides flexibility to produce an inverse design paradigm for generalized metasurface applications without the need for repeated forward simulations. Additionally, the machine learning model is highly effective in reconstructing electric fields, irrespective of the loss function used.
The adoption of neural networks for optical component design has increased rapidly in recent years. In this design framework, the numerical simulation of optical wave propagation and material wave modulation are encoded directly as layers of a neural network. This direct encoding enables the optimization of physical quantities (e.g., the transmissivity values of the diffractive optical elements) by gradient descent and the backpropagation algorithm. For the body of work which uses these networks for simulation and optimization, there is a tendency to treat the training process as identical to traditional deep neural networks. However, to the best of our knowledge, there is yet an explicit evaluation of training parameters to support this intuition. This work aims to help fill this gap by providing an exploration and evaluation of data variety to help accelerate those in the community who wish to use this emerging design framework.The application of neural networks in optical component design has witnessed rapid growth in recent years. This design framework encodes the numerical simulation of optical wave propagation and material wave modulation directly within neural network layers, enabling the optimization of physical quantities, such as transmissivity values of diffractive optical elements, through gradient descent and backpropagation algorithms. Physics-informed neural networks have been employed in designing diffractive deep neural networks, optimizing holograms for near-eye displays and creating multi-objective traditional optics. However, there remains a lack of evaluation for training parameters, and discrete sampling considerations are often overlooked. To address these gaps, this study examines the impact of dataset variety on physics-informed neural networks in optimizing lenses that either satisfy or violate the Nyquist sampling criteria. Results show that increased data variety enhances optimized lens performance across all cases. Optimized lenses demonstrate improved imaging performance by reducing diffraction orders present in aliased analytical lenses. Moreover, we reveal that low data variety leads to overfit lenses that function as selective imagers, providing valuable insights for future lens design and optimization.
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