Abstract 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.

中文翻译：

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基于ResNet生成神经网络的光子结构多目标分类全局优化

摘要 我们展示了基于全局优化网络 (GLOnets) 的深度生成神经网络可以配置为执行光子设备的多目标和分类全局优化。残差网络方案使 GLOnets 能够从在优化过程的早期正确搜索整个设计空间所需的深层架构演变为生成全局最优设备的狭窄分布的浅层网络。作为概念验证演示，我们采用我们的方法来设计由多种材料类型组成的薄膜堆栈。具有已知全局优化抗反射结构的基准测试表明，与传统算法相比，GLOnets 可以以快几个数量级的速度找到全局最优值。我们还展示了我们的方法在复杂设计任务中的实用性，并将其应用于白炽灯滤光片。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。