Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
PLoS One. 2024 Aug 22;19(8):e0307211. doi: 10.1371/journal.pone.0307211. eCollection 2024.
Modern optical systems are important components of contemporary electronics and communication technologies, and the design of new systems has led to many innovative breakthroughs. This paper introduces a novel application based on deep reinforcement learning, D3QN, which is a combination of the Dueling Architecture and Double Q-Network methods, to design distributed Bragg reflectors (DBRs). Traditional design methods are based on time-consuming iterative simulations, whereas D3QN is designed to optimize the multilayer structure of DBRs. This approach enabled the reflectance performance and compactness of the DBRs to be improved. The reflectance of the DBRs designed using D3QN is 20.5% higher compared to designs derived from the transfer matrix method (TMM), and these DBRs are 61.2% smaller in terms of their size. These advancements suggest that deep reinforcement learning, specifically the D3QN methodology, is a promising new method for optical design and is more efficient than traditional techniques. Future research possibilities include expansion to 2D and 3D design structures, where increased design complexities could likely be addressed using D3QN or similar innovative solutions.
现代光学系统是当代电子和通信技术的重要组成部分，新系统的设计带来了许多创新突破。本文介绍了一种基于深度强化学习的新应用，即 D3QN，它结合了 Dueling 架构和 Double Q-Network 方法，用于设计分布式布拉格反射器（DBR）。传统的设计方法基于耗时的迭代模拟，而 D3QN 旨在优化 DBR 的多层结构。这种方法可以提高 DBR 的反射率性能和紧凑性。使用 D3QN 设计的 DBR 的反射率比基于传输矩阵方法（TMM）的设计高 20.5%，并且这些 DBR 的尺寸小 61.2%。这些进展表明，深度强化学习，特别是 D3QN 方法，是一种有前途的光学设计新方法，比传统技术更高效。未来的研究可能性包括扩展到 2D 和 3D 设计结构，其中使用 D3QN 或类似的创新解决方案可能可以解决更高的设计复杂性。
