Region-specified inverse design of absorption and scattering in nanoparticles by using machine learning
Region-specified inverse design of absorption and scattering in nanoparticles by using machine learning
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DOI：
10.1088/2515-7647/acc7e5
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Alex Vallone;N. M. Estakhri;N. Mohammadi Estakhri
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文献类型：
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作者：
Alex Vallone;N. M. Estakhri;N. Mohammadi Estakhri
机器学习为光子结构的正向建模和逆向设计提供了一个很有前途的平台。依靠数据驱动的方法，机器学习对于无法为复杂问题导出分析解决方案的情况特别有吸引力。最近有大量的兴趣在构建适合不同电磁问题的机器学习模型。在这项工作中，我们适应了区域指定的设计方法的多层纳米粒子的逆向设计。鉴于电磁问题数据集生成的计算成本很高，我们专门研究了通过逆卷积神经网络中的随机区域规范增强的小训练数据集的情况。训练的模型用于设计具有高吸收水平和不同吸收与散射比率的纳米颗粒。中心设计波长在350 - 700 nm范围内移动，无需重新训练。我们讨论了波长，颗粒大小和训练数据集大小对模型性能的影响。我们的方法可能会发现有趣的应用在生物，化学和光学应用的多层纳米粒子的设计，以及低散射吸收体和天线的设计。
Machine learning provides a promising platform for both forward modeling and the inverse design of photonic structures. Relying on a data-driven approach, machine learning is especially appealing for situations when it is not feasible to derive an analytical solution for a complex problem. There has been a great amount of recent interest in constructing machine learning models suitable for different electromagnetic problems. In this work, we adapt a region-specified design approach for the inverse design of multilayered nanoparticles. Given the high computational cost of dataset generation for electromagnetic problems, we specifically investigate the case of a small training dataset, enhanced via random region specification in an inverse convolutional neural network. The trained model is used to design nanoparticles with high absorption levels and different ratios of absorption over scattering. The central design wavelength is shifted across 350–700 nm without re-training. We discuss the implications of wavelength, particle size, and the training dataset size on the performance of the model. Our approach may find interesting applications in the design of multilayer nanoparticles for biological, chemical, and optical applications as well as the design of low-scattering absorbers and antennas.