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The design of metamaterials which support unique optical responses is the basis for most thin-film nanophotonics applications.
Theeten, J.; Aspnes, D. Ellipsometry in Thin Film Analysis. Annu. Rev. Mater. Sci. 1981
1981
Earlier work this paper cites.
Chilwell, J.; Hodgkinson, I. Thin-films field-transfer matrix theory of planar multilayer waveguides and reflection from prism-loaded waveguides. J. Opt. Soc. Am. A 1984
1984
Earlier work this paper cites.
Hornik, K.; Stinchcombe, M.; White, H. Multilayer feedforward networks are universal approximators. Neural Networks 1989
1989
Earlier work this paper cites.
Tikhonravov, A. V.; Trubetskov, M. K.; DeBell, G. W. Application of the needle optimization technique to the design of optical coatings. Appl. Opt. 1996
1996
Earlier work this paper cites.
Sullivan, B. T.; Dobrowolski, J. A. Implementation of a numerical needle method for thin-film design. Appl. Opt. 1996
1996
Earlier work this paper cites.
Lawrence, S.; Giles, C. L.; Ah Chung Tsoi,; Back, A. D. Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks 1997
1997
Earlier work this paper cites.
Storn, R.; Price, K. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. J. Glob. Optim. 1997
1997
Earlier work this paper cites.
Tikhonravov, A. V.; Trubetskov, M. K.; DeBell, G. W. Optical coating design approaches based on the needle optimization technique. Appl. Opt. 2007
2007
Earlier work this paper cites.
Froemming, N. S.; Henkelman, G. Optimizing core-shell nanoparticle catalysts with a genetic algorithm. J. Chem. Phys. 2009
2009
Earlier work this paper cites.
Liu, Y.; Zhang, X. Metamaterials: a new frontier of science and technology. Chem. Soc. Rev. 2011
2011
Earlier work this paper cites.
Anzengruber, S. W.; Klann, E.; Ramlau, R.; Tonova, D. Numerical methods for the design of gradient-index optical coatings. Appl. Opt. 2012
2012
Earlier work this paper cites.
Yu, N.; Capasso, F. Flat optics with designer metasurfaces. Nat. Mater. 2014
2014
Earlier work this paper cites.
Gallinet, B.; Butet, J.; Martin, O. J. F. Numerical methods for nanophotonics: standard problems and future challenges. Laser Photonics Rev. 2015
2015
Earlier work this paper cites.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015
2015
Earlier work this paper cites.
Liu, W.; Wang, Z.; Liu, X.; Zeng, N.; Liu, Y.; Alsaadi, F. E. A survey of deep neural network architectures and their applications. Neurocomputing 2017
2016
Earlier work this paper cites.
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016; pp 770–778, DOI: https://doi.org/10.1109/CVPR.2016.90
2016
Earlier work this paper cites.
Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 2017
2017
Earlier work this paper cites.
Yao, K.; Unni, R.; Zheng, Y. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale. Nanophotonics 2019
2018
Cited alongside, same era.
Molesky, S.; Lin, Z.; Piggott, A.; Jin, W.; Vucković, J.; Rodriguez, A. Inverse Design in Nanophotonics. Nat. Photonics 2018
2018
Cited alongside, same era.
Peurifoy, J.; Shen, Y.; Jing, L.; Yang, Y.; Cano-Renteria, F.; DeLacy, B. G.; Joannopoulos, J. D.; Tegmark, M.; Soljačić, M. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv. 2018
2018
Cited alongside, same era.
Pilozzi, L.; Farrelly, F. A.; Marcucci, G.; Conti, C. Machine learning inverse problem for topological photonics. Commun Phys 2018
2018
Cited alongside, same era.
Liu, D.; Tan, Y.; Khoram, E.; Yu, Z. Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures. ACS Photonics 2018
Balin, I.; Garmider, V.; Long, Y.; Abdulhalim, I. Training artificial neural network for optimization of nanostructured VO2-based smart window performance. Opt. Express 2019
2019
Later among the works it cites.
Li, X.; Shu, J.; Gu, W.; Gao, L. Deep neural network for plasmonic sensor modeling. Opt. Mater. Express 2019
2019
Later among the works it cites.
Sajedian, I.; Kim, J.; Rho, J. Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks. Microsyst. Nanoeng. 2019
2019
Later among the works it cites.
So, S.; Rho, J. Designing nanophotonic structures using conditional deep convolutional generative adversarial networks. Nanophotonics 2019
2019
Later among the works it cites.
Ma, W.; Liu, Z.; Kudyshev, Z. A.; Boltasseva, A.; Cai, W.; Liu, Y. Deep learning for the design of photonic structures. Nat. Photonics 2020
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2018
Cited alongside, same era.
Zhou, J.; Huang, B.; Yan, Z.; Bünzli, J.-C. G. Emerging role of machine learning in light-matter interaction. Light: Sci. Appl. 2018
2018
Cited alongside, same era.
Malkiel, I.; Mrejen, M.; Nagler, A.; Arieli, U.; Wolf, L.; Suchowski, H. Plasmonic Nanostructure Design and Characterization via Deep Learning. Light: Sci. Appl. 2018
2018
Cited alongside, same era.
Ma, W.; Cheng, F.; Liu, Y. Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials. ACS Nano 2018
2018
Cited alongside, same era.
Asano, T.; Noda, S. Optimization of photonic crystal nanocavities based on deep learning. Opt. Express 2018
2018
Cited alongside, same era.
Mocanu, D. C.; Mocanu, E.; Stone, P.; Nguyen, P. H.; Gibescu, M.; Liotta, A. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nat. Commun. 2018
2018
Cited alongside, same era.
Maccaferri, N.; Zhao, Y.; Isoniemi, T.; Iarossi, M.; Parracino, A.; Strangi, G.; Angelis, F. D. Hyperbolic Meta-Antennas Enable Full Control of Scattering and Absorption of Light. Nano Lett. 2019
2019
Cited alongside, same era.
Sreekanth, K. V.; ElKabbash, M.; Caligiuri, V.; Singh, R.; De Luca, A.; Strangi, G. New Directions in Thin Film Nanophotonics ; Springer Singapore, 2019; DOI: https://doi.org/10.1007/978-981-13-8891-0
2019
Cited alongside, same era.
2020
Later among the works it cites.
Kim, M.-G. Improved Measurement of Thin Film Thickness in Spectroscopic Reflectometer Using Convolutional Neural Networks. Int. J. Precis. Eng. Manuf. 2020
2020
Later among the works it cites.
Jiang, A.; Osamu, Y.; Chen, L. Multilayer optical thin film design with deep Q learning. Sci. Rep. 2020
2020
Later among the works it cites.
Christensen, T.; Loh, C.; Picek, S.; Jakobović, D.; Jing, L.; Fisher, S.; Ceperic, V.; Joannopoulos, J. D.; Soljačić, M. Predictive and Generative Machine Learning Models for Photonic Crystals. Nanophotonics 2020
2020
Later among the works it cites.
Naoto Akashi1, M. T.; Kajikawa, K. Design by neural network of concentric multilayered cylindrical metamaterials. Appl. Phys. Express 2020
2020
Later among the works it cites.
Lin, R.; Zhai, Y.; Xiong, C.; Li, X. Inverse design of plasmonic metasurfaces by convolutional neural network. Opt. Lett. 2020
2020
Later among the works it cites.
Qiu, C.; Wu, X.; Luo, Z.; Yang, H.; Wang, G.; Liu, N.; Huang, B. Simultaneous inverse design continuous and discrete parameters of nanophotonic structures via back-propagation inverse neural network. Opt. Commun. 2021
2020
Later among the works it cites.
Kahn, A.; Sohail, A.; Zahoora, U.; Qureshi, A. S. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 2020
2020
Later among the works it cites.
ElKabbash, M.; Letsou, T.; Jalil, S. A.; Hoffman, N.; Zhang, J.; Rutledge, J.; Lininger, A. R.; Fann, C.-H.; Hinczewski, M.; Strangi, G.; Guo, C. Fano-resonant ultrathin film optical coatings. Nat. Nanotechnol. 2021
2021
Closest in time.
Liu, Z.; Zhu, D.; Raju, L.; Cai, W. Tackling Photonic Inverse Design with Machine Learning. Adv. Sci. 2021
2021
Closest in time.
Kojima, K.; Tahersima, M. H.; Koike-Akino, T.; Jha, D. K.; Tang, Y.; Wang, Y.; Parsons, K. Deep Neural Networks for Inverse Design of Nanophotonic Devices. J. Lightwave Technol. 2021
2021
Closest in time.
Yeung, C.; Tsai, R.; Pham, B.; King, B.; Kawagoe, Y.; Ho, D.; Liang, J.; Knight, M. W.; Raman, A. P. Global Inverse Design across Multiple Photonic Structure Classes Using Generative Deep Learning. Adv. Opt. Mater. 2021
2021
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