scholarly journals Topological inverse design of nanophotonic devices with energy constraint

2021 ◽  
Author(s):  
Guowu Zhang ◽  
Dan-Xia Xu ◽  
Yuri Grinberg ◽  
Odile Liboiron-Ladouceur
2021 ◽  
pp. 1-1
Author(s):  
Keisuke Kojima ◽  
Mohammad H. Tahersima ◽  
Toshiaki Koike-Akino ◽  
Devesh K. Jha ◽  
Yingheng Tang ◽  
...  

Nanophotonics ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Jie Huang ◽  
Hansi Ma ◽  
Dingbo Chen ◽  
Huan Yuan ◽  
Jinping Zhang ◽  
...  

AbstractNanophotonic devices with high densities are extremely attractive because they can potentially merge photonics and electronics at the nanoscale. However, traditional integrated photonic circuits are designed primarily by manually selecting parameters or employing semi-analytical models. Limited by the small parameter search space, the designed nanophotonic devices generally have a single function, and the footprints reach hundreds of microns. Recently, novel ultra-compact nanophotonic devices with digital structures were proposed. By applying inverse design algorithms, which can search the full parameter space, the proposed devices show extremely compact footprints of a few microns. The results from many groups imply that digital nanophotonics can achieve not only ultra-compact single-function devices but also miniaturized multi-function devices and complex functions such as artificial intelligence operations at the nanoscale. Furthermore, to balance the performance and fabrication tolerances of such devices, researchers have developed various solutions, such as adding regularization constraints to digital structures. We believe that with the rapid development of inverse design algorithms and continuous improvements to the nanofabrication process, digital nanophotonics will play a key role in promoting the performance of nanophotonic integration. In this review, we uncover the exciting developments and challenges in this field, analyse and explore potential solutions to these challenges and provide comments on future directions in this field.


Author(s):  
Mediha Tutgun ◽  
Yusuf Abdulaziz Yilmaz ◽  
Aydan Yeltik ◽  
Done Yilmaz ◽  
Ahmet Mesut Alpkilic ◽  
...  

Author(s):  
Alexander Y. Piggott ◽  
Jesse Lu ◽  
Konstantinos G. Lagoudakis ◽  
Jan Petykiewicz ◽  
Thomas M. Babinec ◽  
...  

2020 ◽  
Vol 8 (4) ◽  
pp. 528 ◽  
Author(s):  
Kaiyuan Wang ◽  
Xinshu Ren ◽  
Weijie Chang ◽  
Longhui Lu ◽  
Deming Liu ◽  
...  

Nanophotonics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 1041-1057 ◽  
Author(s):  
Sunae So ◽  
Trevon Badloe ◽  
Jaebum Noh ◽  
Jorge Bravo-Abad ◽  
Junsuk Rho

AbstractDeep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mapping between the topology and composition of arbitrary nanophotonic structures and their associated functional properties. In this paper, we have discussed the recent progress in the application of deep learning to the inverse design of nanophotonic devices, mainly focusing on the three existing learning paradigms of supervised-, unsupervised-, and reinforcement learning. Deep learning forward modelling i.e. how artificial intelligence learns how to solve Maxwell’s equations, is also discussed, along with an outlook of this rapidly evolving research area.


ACS Photonics ◽  
2020 ◽  
Vol 7 (8) ◽  
pp. 2190-2196
Author(s):  
Yannick Augenstein ◽  
Carsten Rockstuhl

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