This work proposed a universal platform for ultra-sensitive detection, which integrates sensory data acquisition and spectral feature extraction into a single machine learning (ML) hardware.We fabricated and tested the sensing platform in glucose detection tasks, reaching 5 orders of magnitude higher sensitivity compared to the state-of-the-art. This technology requires no bulky spectral measuring devices such as a spectrum analyzer but a standard off-the-shelf camera to achieve real-time detection of the glucose concentration.
In this work, we propose a novel framework for large-scale aperiodic nanophotonic inverse design utilizing an experimental machine-learning technique. With this technique, we create an extensive dataset of 10 million experimental structures for enhanced flat-optics design. This largest publicly available inverse design dataset, achieved through electron beam lithography, bypasses the extensive computational demand of first-principle simulations. Experimental acquisition ensures the dataset embodies real-world variances, leading to ML models with a ten-fold improved prediction accuracy in optical responses, drastically reducing validation RMSE from 0.012 to 0.0018. With this dataset, we developed a framework for large-scale aperiodic photonics design capable of designing tens of structures per second. We demonstrate the efficiency of the proposed technique by creating a large (3x3mm) aperiodic photonic structure composed of >10000 individual structures with pre-defined transmission/reflection responses.
We present a framework for optical metrology in which, through the hardware implementation of artificial intelligence via metasurfaces, a conventional camera becomes a metrology system capable of retrieving observables from a light beam. We show the experimental realization of a prototype of this system and the results of its use for measuring the properties of thin films.
In this work we make use of an inverse design methodology for the design of high efficiency deformation robust flat optics. Our approach leverages neural network predictors trained to quickly estimate the results of finite difference time domain (FDTD) simulations. By rapidly exploring the solution space, we find geometries that exhibit an optical response tolerant to dimensional errors. We validate our approach by fabricating and characterizing flat optics polarizers on top of polyamide tape. The devices exhibit a polarization efficiency of 85% over a 200 nm bandwidth and retain high performance when subjected to large deformations, in contrast to a control non-robust design.
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