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Abstract Over the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable “by design” effective electromagnetic properties. The current state-of-the-art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or metaatoms. Such an approach cannot provide the global solution to a complex optimization problem where metaatom shape, in-plane geometry, out-of-plane architecture, and constituent materials have to be properly chosen to yield the maximum performance. In this work, we present a novel machine learning–assisted global optimization framework for photonic metadevice design. We demonstrate that using an adversarial autoencoder (AAE) coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the metadevice configurations with complex topologies. We showcase the concept of physics-driven compressed design space engineering that introduces advanced regularization into the compressed space of an AAE based on the optical responses of the devices. Beyond the significant advancement of the global optimization schemes, our approach can assist in gaining comprehensive design “intuition” by revealing the underlying physics of the optical performance of metadevices with complex topologies and material compositions.

The recently proposed concept of graphene photodetectors offers remarkable properties such as unprecedented compactness, ultrabroadband detection, and an ultrafast response speed. However, owing to the low optical absorption of pristine monolayer graphene, the intrinsically low responsivity of graphene photodetectors significantly hinders the development of practical devices. To address this issue, numerous efforts have thus far been made to enhance the light–graphene interaction using plasmonic structures. These approaches, however, can be significantly advanced by leveraging the other critical aspect of graphene photoresponsivity enhancement—electrical junction control. It has been reported that the dominant photocarrier generation mechanism in graphene is the photothermoelectric (PTE) effect. Thus, the two energy conversion mechanisms involved in the graphene photodetection process are light-to-heat and heat-to-electricity conversions. In this work, we propose a meticulously designed device architecture to simultaneously enhance the two conversion efficiencies. Specifically, a gap plasmon structure is used to absorb a major portion of the incident light to induce localized heating, and a pair of split gates is used to produce a p-n junction in graphene to augment the PTE current generation. The gap plasmon structure and the split gates are designed to share common key components so that the proposed device architecture concurrently realizesmore »both optical and electrical enhancements. We experimentally demonstrate the dominance of the PTE effect in graphene photocurrent generation and observe a 25-fold increase in the generated photocurrent compared to the un-enhanced cases. While further photocurrent enhancement can be achieved by applying a DC bias, the proposed device concept shows vast potential for practical applications.

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