Optical multi-layer thin films are widely used in optical and energy applications requiring photonic designs. Engineers often design such structures based on their physical intuition. However, solely relying on human experts can be time-consuming and may lead to sub-optimal designs, especially when the design space is large. In this work, we frame the multi-layer optical design task as a sequence generation problem. A deep sequence generation network is proposed for efficiently generating optical layer sequences. We train the deep sequence generation network with proximal policy optimization to generate multi-layer structures with desired properties. The proposed method is applied to two energy applications. Our algorithm successfully discovered high-performance designs, outperforming structures designed by human experts in task 1, and a state-of-the-art memetic algorithm in task 2.

光学多层薄膜广泛用于需要光子设计的光学和能源应用中。工程师经常根据自己的直觉设计此类结构。但是，仅依靠人类专家会很耗时，并且可能导致次优设计，尤其是在设计空间很大的情况下。在这项工作中，我们将多层光学设计任务构架为一个序列生成问题。提出了用于有效地产生光学层序列的深度序列产生网络。我们使用近端策略优化来训练深度序列生成网络，以生成具有所需属性的多层结构。该方法适用于两种能源应用。我们的算法成功发现了高性能设计，性能优于人类专家在任务1中设计的结构