Abstract
AbstractWe 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.
Highlights
Inverse algorithms are among the most effective methods for designing efficient, multifunctional photonic devices [1–3]
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
We show that Res-GLOnets are effective and efficient global optimizers for the multiobjective, categorical design of thin-film stacks
Summary
Inverse algorithms are among the most effective methods for designing efficient, multifunctional photonic devices [1–3]. The reason is because the design space is nonconvex and contains multiple local optima, and even devices based on advanced gradient-based optimization methods cannot help a neural network search for the global optimum. In this vein, global optimization networks (GLOnets) have been developed to perform the nonconvex global optimization of free-form photonic devices [19, 20]. Design methods based on physical intuition result in limited performance, and they are generally difficult to scale to aperiodic thin-film stacks comprising many layers To address these limitations, various global optimization approaches have been explored, including the Monte Carlo approach [31], particle swarm optimization [32], needle optimization [33–35], and the memetic algorithm [21]. These methods are all derivative-free global optimization algorithms that search the design space through the evaluation of a batch of samples without any gradient calculations, limiting their ability to reliably solve for the global optimum
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