While deep learning has demonstrated tremendous potential for photonic device design, it often demands a large amount of labeled data to train these deep neural network models. Preparing these data requires high-resolution numerical simulations or experimental measurements and cost significant, if not prohibitive, time and resources. In this work, we present a highly efficient inverse design method that combines deep neural networks with a genetic algorithm to optimize the geometry of photonic devices in the polar coordinate system. The method requires significantly less training data compared with previous inverse design methods. We implement this method to design several ultra-compact silicon photonics devices with challenging properties including power splitters with uncommon splitting ratios, a TE mode converter, and a broadband power splitter. These devices are free of the features beyond the capability of photolithography and generally in compliance with silicon photonics fabrication design rules..
Micro- and nanomechanical resonators have emerged as promising platforms for sensing a broad range of physical properties, such as mass, force, torque, magnetic field, and acceleration. The sensing performance relies critically on the motional mass, mechanical frequency, and linewidth of the mechanical resonator. Herein, we demonstrate a hetero optomechanical crystal (OMC) cavity based on a silicon nanobeam structure. The cavity supports phonon lasing in a fundamental mechanical mode with a frequency of 5.91 GHz, an effective mass of 116 fg, and a mechanical linewidth narrowing in the range from 3.3 MHz to 5.2 kHz, while the optomechanical coupling rate is as high as 1.9 MHz. With this phonon laser, on-chip sensing can be predicted with a resolution of
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