多层薄膜模型的嵌套深度迁移学习。

IF 18.8 1区 物理与天体物理 Q1 OPTICS
Advanced Photonics Pub Date : 2024-10-01 Epub Date: 2024-10-08 DOI:10.1117/1.ap.6.5.056006
Rohit Unni, Kan Yao, Yuebing Zheng
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引用次数: 0

摘要

机器学习技术在纳米光子学研究中越来越受欢迎,被应用于预测光学性质和逆向设计结构。然而,一个限制是获取训练数据的成本,因为复杂的结构需要耗时的模拟。为了解决这个问题,研究人员已经探索了使用迁移学习,其中预训练的网络可以用更少的数据促进相关任务的收敛,但应用于更困难的任务仍然有限。在这项工作中,提出了一种嵌套迁移学习方法,训练模型来预测越来越复杂的结构,每个模型之间都有迁移,每一步使用的数据很少。这使得建模具有比以前报道的更高的光学复杂性的薄膜堆栈成为可能。对于正演模型,采用双向递归神经网络,该网络在序列输入建模方面具有优势。对于逆模型,采用卷积混合密度网络。在这两种情况下,每一层的材料选择都是宽松的,使方法更加通用。最终的嵌套传递模型在检索复杂的任意光谱和匹配特定应用的理想光谱方面显示出很高的准确性,同时保持数据要求适中。我们的嵌套迁移学习方法代表了解决数据获取挑战的有前途的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nested deep transfer learning for modeling of multilayer thin films.

Machine learning techniques have gained popularity in nanophotonics research, being applied to predict optical properties, and inversely design structures. However, one limitation is the cost of acquiring training data, as complex structures require time-consuming simulations. To address this, researchers have explored using transfer learning, where pre-trained networks can facilitate convergence with fewer data for related tasks, but application to more difficult tasks is still limited. In this work, a nested transfer learning approach is proposed, training models to predict structures of increasing complexity, with transfer between each model and few data used at each step. This allows modeling thin film stacks with higher optical complexity than previously reported. For the forward model, a bidirectional recurrent neural network is utilized, which excels in modeling sequential inputs. For the inverse model, a convolutional mixture density network is employed. In both cases, a relaxed choice of materials at each layer is introduced, making the approach more versatile. The final nested transfer models display high accuracy in retrieving complex arbitrary spectra and matching idealized spectra for specific applications-focused cases such as selective thermal emitters, while keeping data requirements modest. Our nested transfer learning approach represents a promising avenue for addressing data acquisition challenges.

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来源期刊
CiteScore
22.70
自引率
1.20%
发文量
49
审稿时长
18 weeks
期刊介绍: Advanced Photonics is a highly selective, open-access, international journal that publishes innovative research in all areas of optics and photonics, including fundamental and applied research. The journal publishes top-quality original papers, letters, and review articles, reflecting significant advances and breakthroughs in theoretical and experimental research and novel applications with considerable potential. The journal seeks high-quality, high-impact articles across the entire spectrum of optics, photonics, and related fields with specific emphasis on the following acceptance criteria: -New concepts in terms of fundamental research with great impact and significance -State-of-the-art technologies in terms of novel methods for important applications -Reviews of recent major advances and discoveries and state-of-the-art benchmarking. The journal also publishes news and commentaries highlighting scientific and technological discoveries, breakthroughs, and achievements in optics, photonics, and related fields.
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