Hong Youngjoon, Nicholls David P
Opt Express. 2022 Jun 20;30(13):22901-22910. doi: 10.1364/OE.459295.
A deep learning aided optimization algorithm for the design of flat thin-film multilayer optical systems is developed. The authors introduce a deep generative neural network, based on a variational autoencoder, to perform the optimization of photonic devices. This algorithm allows one to find a near-optimal solution to the inverse design problem of creating an anti-reflective grating, a fundamental problem in material science. As a proof of concept, the authors demonstrate the method's capabilities for designing an anti-reflective flat thin-film stack consisting of multiple material types. We designed and constructed a dielectric stack on silicon that exhibits an average reflection of 1.52 %, which is lower than other recently published experiments in the engineering and physics literature. In addition to its superior performance, the computational cost of our algorithm based on the deep generative model is much lower than traditional nonlinear optimization algorithms. These results demonstrate that advanced concepts in deep learning can drive the capabilities of inverse design algorithms for photonics. In addition, the authors develop an accurate regression model using deep active learning to predict the total reflectivity for a given optical system. The surrogate model of the governing partial differential equations can then be broadly used in the design of optical systems and to rapidly evaluate their behavior.
开发了一种用于设计平面薄膜多层光学系统的深度学习辅助优化算法。作者引入了一种基于变分自编码器的深度生成神经网络，以执行光子器件的优化。该算法使人们能够找到创建抗反射光栅这一材料科学中的基本逆设计问题的近似最优解。作为概念验证，作者展示了该方法设计由多种材料类型组成的抗反射平面薄膜堆叠的能力。我们在硅上设计并构建了一个介质堆叠，其平均反射率为1.52%，低于工程和物理文献中最近发表的其他实验结果。除了其卓越的性能外，我们基于深度生成模型的算法的计算成本远低于传统的非线性优化算法。这些结果表明，深度学习中的先进概念可以推动光子学逆设计算法的能力。此外，作者使用深度主动学习开发了一个精确的回归模型，以预测给定光学系统的总反射率。控制偏微分方程的替代模型随后可广泛用于光学系统的设计并快速评估其行为。
