光学薄膜设计的强化学习方法
A Reinforcement learning method for Optical Thin-Film Design
摘要：
机器学习，特别是深度学习，正在极大地改变与光学薄膜逆向设计相关的方法。这方面的研究绝大多数集中在光学薄膜的参数优化（层厚和结构尺寸）。出现的一个具有挑战性的问题是自动材料搜索。在这项工作中，我们提出了一个新的端到端的光学薄膜逆向设计算法。该方法结合了无监督学习、强化学习（RL）的能力，并包括遗传算法来设计光学薄膜，而无需任何人为干预。此外，通过几个具体的例子，我们已经展示了如何使用这种技术来优化多层太阳能吸收器设备的光谱。
英文摘要：
Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design. The vast majority of this research has focused on the parameter optimization (layer thickness, and structure size) of optical thin-films. A challenging problem that arises is an automated material search. In this work, we propose a new end-to-end algorithm for optical thin-film inverse design. This method combines the ability of unsupervised learning, reinforcement learning(RL) and includes a genetic algorithm to design an optical thin-film without any human intervention. Furthermore, with several concrete examples, we have shown how one can use this technique to optimize the spectra of a multi-layer solar absorber device.
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