通过关联潜空间表征学习,在任意大的形状集中发现深度学习支持的光子纳米结构

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-07-01 DOI:10.1039/D4DD00107A
Sudhanshu Singh, Rahul Kumar, Soumyashree S. Panda and Ravi S. Hegde
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引用次数: 0

摘要

现代纳米制造技术可实现的形状种类繁多,这给选择最佳设计以实现所需的光学响应带来了挑战。虽然深度学习等数据驱动技术有望实现逆向设计,但其适用性往往受到限制,因为它们通常只能探索纳米制造技术所能实现的大量形状中较小的子集。此外,这些模型通常被视为 "黑盒子",在揭示形状与光学响应之间的内在关系方面缺乏透明度。在此,我们介绍了一种专门针对大型、复杂、多样的纳米结构所带来的挑战而量身定制的方法。具体来说,我们在可见光波长范围内工作的周期性硅元表面上演示了我们的方法,并考虑了大量不同的形状集变化。我们的配对变异自动编码器方法有助于创建丰富、连续和参数对齐的形状-响应关系潜在空间表示。我们在两个关键领域展示了我们方法的实用性:1) 实现多方案逆向设计;2)对形状对纳米加工引起的变形的光学响应进行敏感性分析。这种方法代表了数据驱动设计技术的重大进步,进一步释放了纳米光子学的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Deep-learning enabled photonic nanostructure discovery in arbitrarily large shape sets via linked latent space representation learning†

The vast array of shapes achievable through modern nanofabrication technologies presents a challenge in selecting the most optimal design for achieving a desired optical response. While data-driven techniques, such as deep learning, hold promise for inverse design, their applicability is often limited as they typically explore only smaller subsets of the extensive range of shapes feasible with nanofabrication. Additionally, these models are often regarded as ‘black boxes,’ lacking transparency in revealing the underlying relationship between the shape and optical response. Here, we introduce a methodology tailored to address the challenges posed by large, complex, and diverse sets of nanostructures. Specifically, we demonstrate our approach in the context of periodic silicon metasurfaces operating in the visible wavelength range, considering large and diverse shape set variations. Our paired variational autoencoder method facilitates the creation of rich, continuous, and parameter-aligned latent space representations of the shape–response relationship. We showcase the practical utility of our approach in two key areas: (1) enabling multiple-solution inverse design and (2) conducting sensitivity analyses on a shape's optical response to nanofabrication-induced distortions. This methodology represents a significant advancement in data-driven design techniques, further unlocking the application potential of nanophotonics.

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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