A novel conditional variational autoencoder (CVAE) model for designing nanopatterned integrated photonic components is proposed. In particular, it is shown that prediction capability of the CVAE model can be significantly improved by adversarial censoring and active learning. Moreover, generation of nanopatterned power splitters with arbitrary splitting ratios and 550 nm broadband optical responses from 1250 to 1800 nm are demonstrated. Nanopatterned power splitters with footprints of 2.25 × 2.25 μm2 and 20 × 20 etch hole positions are the design space, with each hole position assuming a radius from a range of radii. Designed nanopatterned power splitters using methods presented herein demonstrate an overall transmission of about 90% across the operating bandwidth from 1250 to 1800 nm. To the best of authors' knowledge, this is the first time that a state‐of‐the‐art CVAE deep neural network model is successfully used to design a physical device.
Diffractive optical neural networks have shown promising advantages over electronic circuits for accelerating modern machine learning (ML) algorithms. However, it is challenging to achieve fully programmable all‐optical implementation and rapid hardware deployment. Here, a large‐scale, cost‐effective, complex‐valued, and reconfigurable diffractive all‐optical neural networks system in the visible range is demonstrated based on cascaded transmissive twisted nematic liquid crystal spatial light modulators. The employment of categorical reparameterization technique creates a physics‐aware training framework for the fast and accurate deployment of computer‐trained models onto optical hardware. Such a full stack of hardware and software enables not only the experimental demonstration of classifying handwritten digits in standard datasets, but also theoretical analysis and experimental verification of physics‐aware adversarial attacks onto the system, which are generated from a complex‐valued gradient‐based algorithm. The detailed adversarial robustness comparison with conventional multiple layer perceptrons and convolutional neural networks features a distinct statistical adversarial property in diffractive optical neural networks. The developed full stack of software and hardware provides new opportunities of employing diffractive optics in a variety of ML tasks and in the research on optical adversarial ML.
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