Abstract
We propose and demonstrate a six-hole tapered silicon photonic crystal nanobeam cavity with a theoretical high quality (Q) factor and an ultrasmall mode volume based on machine learning. The crucial element to efficiently obtain high Q factors is to take the prediction result of the designed neural network as the fitness function of the genetic algorithm, whose evolution direction is developing towards higher fitness. Consequently, by combining the neural network and genetic algorithm iteration, an optimized photonic crystal nanobeam cavity with a theoretical Q factor (Qth) as high as 1.2 × 108 and an ultrasmall mode volume of 0.32(λ/n)3 is obtained. Leveraging the resonant scattering optical method, the cavity experimental Q factor (Qexp) is measured as 2.17 × 106, which is a record high experimental Q factor of silicon photonic crystal nanobeam cavity with maintaining an ultrasmall mode volume of 0.32(λ/n)3 and an ultra-compact device size of 6 μm2. Owing to the ultra-high Q factor-to-mode volume ratio, the proposed photonic crystal nanobeam cavities could extremely enhance the interactions between light and matter, which have extensive important applications in low-threshold optical lasers, high-resolution filters and sensors.
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