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Article

Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network

by 1,†, 2,†, 1,3, 1,3, 3,* and 1,*
1
Shenzhen Key Laboratory of Semiconductor Lasers, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
2
The Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen 518172, China
3
Department of Computer and Information Engineering, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Nicola Calabretta
Nanomaterials 2022, 12(8), 1372; https://doi.org/10.3390/nano12081372
Received: 28 February 2022 / Revised: 8 April 2022 / Accepted: 14 April 2022 / Published: 16 April 2022
The design of nanophotonic structures based on deep learning is emerging rapidly in the research community. Design methods using Deep Neural Networks (DNN) are outperforming conventional physics-based simulations performed iteratively by human experts. Here, a self-adaptive and regularized DNN based on Convolutional Neural Networks (CNNs) for the smart and fast characterization of nanophotonic structures in high-dimensional design parameter space is presented. This proposed CNN model, named LRS-RCNN, utilizes dynamic learning rate scheduling and L2 regularization techniques to overcome overfitting and speed up training convergence and is shown to surpass the performance of all previous algorithms, with the exception of two metrics where it achieves a comparable level relative to prior works. We applied the model to two challenging types of photonic structures: 2D photonic crystals (e.g., L3 nanocavity) and 1D photonic crystals (e.g., nanobeam) and results show that LRS-RCNN achieves record-high prediction accuracies, strong generalizibility, and substantially faster convergence speed compared to prior works. Although still a proof-of-concept model, the proposed smart LRS-RCNN has been proven to greatly accelerate the design of photonic crystal structures as a state-of-the-art predictor for both Q-factor and V. It can also be modified and generalized to predict any type of optical properties for designing a wide range of different nanophotonic structures. The complete dataset and code will be released to aid the development of related research endeavors. View Full-Text
Keywords: nanophotonic structures; photonic crystal nanocavities; nanoscale lasers; deep learning; modeling and characterization; neural networks; inverse design nanophotonic structures; photonic crystal nanocavities; nanoscale lasers; deep learning; modeling and characterization; neural networks; inverse design
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MDPI and ACS Style

Li, R.; Gu, X.; Shen, Y.; Li, K.; Li, Z.; Zhang, Z. Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network. Nanomaterials 2022, 12, 1372. https://doi.org/10.3390/nano12081372

AMA Style

Li R, Gu X, Shen Y, Li K, Li Z, Zhang Z. Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network. Nanomaterials. 2022; 12(8):1372. https://doi.org/10.3390/nano12081372

Chicago/Turabian Style

Li, Renjie, Xiaozhe Gu, Yuanwen Shen, Ke Li, Zhen Li, and Zhaoyu Zhang. 2022. "Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network" Nanomaterials 12, no. 8: 1372. https://doi.org/10.3390/nano12081372

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