Photonic Crystal Nanobeam Cavity With a High Experimental Q Factor Exceeding Two Million Based on Machine Learning

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 highQfactors 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 theoreticalQfactor (Qth) as high as 1.2 × 108and an ultrasmall mode volume of 0.32(λ/n)3is obtained. Leveraging the resonant scattering optical method, the cavity experimentalQfactor (Qexp) is measured as 2.17 × 106, which is a record high experimentalQfactor of silicon photonic crystal nanobeam cavity with maintaining an ultrasmall mode volume of 0.32(λ/n)3and an ultra-compact device size of 6 μm2. Owing to the ultra-highQfactor-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.