scholarly journals Inverse Design of Nanophotonic Devices with Structural Integrity

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

Author(s):  
Peixin Hu ◽  
Mehrdad Zangeneh ◽  
Benjamin Choo ◽  
Mohammad Rahmati

The application of 3D inverse design to transonic fans can offer designers many advantages in terms of reduction in design time and providing a more direct means of using the insight obtained into flow physics from CFD computations directly in the design process. A number of papers on application of inverse design method to transonic fans have already been reported. However, in order to apply this approach in product design a number of issues need to be addressed. For example, how can the method be used to affect and control the fan rotor characteristics? The robustness of the method and its ability to deal with accurate representation of leading and trailing edges, as well as tip clearance flow. In this paper the further enhancement of the 3D viscous transonic inverse design code TURBOdesign-2 and its application to the re-design of NASA37 and NASA67 rotors will be described. In this inverse design method the blade geometry can be computed by the specification of the blade loading (meridional derivative of rVθ) or the pressure loading. In both cases the blade normal thickness is specified to ensure structural integrity of the design. Improvements to the code include implementation of full approximation storage (FAS) multigrid technique in the solver, which increases the speed of the computation. This method allows the modification of blade thickness and pressure loading by B-splines. In addition improvements have been made in the treatment of proper leading edge geometry. Two well known examples of NASA 67 and NASA 37 rotors are used to provide a step-by-step guide to the application of the method to the design of transonic fan rotors. Improved designs are validated by commercial CFD code CFX.


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.


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