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.

中文翻译：

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用于高效光子器件设计的基于遗传算法的深度神经网络

虽然深度学习在光子设备设计方面展现了巨大的潜力，但它通常需要大量标记数据来训练这些深度神经网络模型。准备这些数据需要高分辨率的数值模拟或实验测量，并且花费大量时间和资源，如果不是令人望而却步的话。在这项工作中，我们提出了一种高效的逆向设计方法，该方法将深度神经网络与遗传算法相结合，以优化极坐标系中光子器件的几何形状。与以前的逆向设计方法相比，该方法需要的训练数据要少得多。我们采用这种方法设计了几种具有挑战性特性的超紧凑型硅光子器件，包括具有不常见分光比的功率分配器、TE 模式转换器和宽带功率分配器。