Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks
Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks
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DOI：
10.1515/nanoph-2020-0407
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发表时间：
2021-01-01
期刊：
影响因子：
7.5
通讯作者：
Fan, Jonathan A.
中科院分区：
文献类型：
--
作者：
Jiang, Jiaqi;Fan, Jonathan A.
我们表明，基于全局优化网络（GLOnets）的深度生成神经网络可以被配置为执行光子器件的多目标和分类全局优化。剩余网络方案使GLOnets能够从深度架构（需要在优化过程的早期适当搜索整个设计空间）发展到浅层网络（生成全局最优设备的窄分布）。作为概念验证演示，我们调整我们的方法来设计由多种材料类型组成的薄膜堆栈。与已知的全局优化的网络结构的基准测试表明，GLOnets可以找到全局最优的数量级更快的速度相比，传统的算法。我们还展示了我们的方法在复杂的设计任务中的实用性，其应用程序的白炽灯滤光片。这些结果表明，深度学习中的先进概念可以推动光子学逆向设计算法的能力。
We show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the optimization process, to a shallow network that generates a narrow distribution of globally optimal devices. As a proof-of-concept demonstration, we adapt our method to design thin-film stacks consisting of multiple material types. Benchmarks with known globally optimized antireflection structures indicate that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. We also demonstrate the utility of our method in complex design tasks with its application to incandescent light filters. These results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.