scholarly journals Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures

ACS Photonics ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 1365-1369 ◽  
Author(s):  
Dianjing Liu ◽  
Yixuan Tan ◽  
Erfan Khoram ◽  
Zongfu Yu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Inverse Design ◽  
Nanophotonic Structures

Deep Neural Networks for Inverse Design of Nanophotonic Devices

2021 ◽  
pp. 1-1
Author(s):  
Keisuke Kojima ◽  
Mohammad H. Tahersima ◽  
Toshiaki Koike-Akino ◽  
Devesh K. Jha ◽  
Yingheng Tang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Inverse Design ◽  
Nanophotonic Devices

Deep Neural Network Inverse-Design for Long Wave Infrared Hyperspectral Imaging

2021 ◽  
Vol 35 (11) ◽  
pp. 1336-1337
Author(s):  
Clayton Fowler ◽  
Sensong An ◽  
Bowen Zheng ◽  
Hong Tang ◽  
Hang Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Electromagnetic Waves ◽  
Deep Neural Networks ◽  
Structural Parameters ◽  
Inverse Design ◽  
Metal Insulator ◽  
Complex Interactions ◽  
Metal Insulator Metal ◽  
Long Wave Infrared

This paper presents a deep learning approach for the inverse-design of metal-insulator-metal metasurfaces for hyperspectral imaging applications. Deep neural networks are able to compensate for the complex interactions between electromagnetic waves and metastructures to efficiently produce design solutions that would be difficult to obtain using other methods. Since electromagnetic spectra are sequential in nature, recurrent neural networks are especially suited for relating such spectra to structural parameters.

scholarly journals Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

2021 ◽  
Vol 11 (9) ◽  
pp. 3822
Author(s):  
Simei Mao ◽  
Lirong Cheng ◽  
Caiyue Zhao ◽  
Faisal Nadeem Khan ◽  
Qian Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Silicon Photonics ◽  
Design Methodology ◽  
Degrees Of Freedom ◽  
Deep Neural Networks ◽  
Low Cost ◽  
Optimization Algorithms ◽  
Optimization Methods ◽  
Inverse Design ◽  
Iterative Optimization

Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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