Multiplexing the aperture of a metasurface: inverse design via deep-learning-forward genetic algorithm

2020 ◽  
Vol 53 (45) ◽  
pp. 455002
Author(s):  
Ruichao Zhu ◽  
Tianshuo Qiu ◽  
Jiafu Wang ◽  
Sai Sui ◽  
Yongfeng Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Inverse Design ◽  
Learning Forward

scholarly journals Deep learning enabled inverse design in nanophotonics

Nanophotonics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 1041-1057 ◽  
Author(s):  
Sunae So ◽  
Trevon Badloe ◽  
Jaebum Noh ◽  
Jorge Bravo-Abad ◽  
Junsuk Rho
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Language Processing ◽  
Research Area ◽  
Inverse Design ◽  
Complex Data ◽  
Nanophotonic Devices ◽  
Nature Language Processing ◽  
Nanophotonic Structures ◽  
Learning Forward

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.

scholarly journals A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abhishek Mall ◽  
Abhijeet Patil ◽  
Amit Sethi ◽  
Anshuman Kumar
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Design Space ◽  
Optical Response ◽  
Inverse Design ◽  
Electromagnetic Response ◽  
Design And Optimization ◽  
Half Wave ◽  
Wide Range ◽  
Optimization Mechanism

Abstract The conventional approach to nanophotonic metasurface design and optimization for a targeted electromagnetic response involves exploring large geometry and material spaces. This is a highly iterative process based on trial and error, which is computationally costly and time consuming. Moreover, the non-uniqueness of structural designs and high non-linearity between electromagnetic response and design makes this problem challenging. To model this unintuitive relationship between electromagnetic response and metasurface structural design as a probability distribution in the design space, we introduce a framework for inverse design of nanophotonic metasurfaces based on cyclical deep learning (DL). The proposed framework performs inverse design and optimization mechanism for the generation of meta-atoms and meta-molecules as metasurface units based on DL models and genetic algorithm. The framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization, and generate optimized structural designs while simultaneously performing forward and inverse design tasks. A selection and evaluation of generated structural designs is performed by the genetic algorithm to construct a desired optical response and design space that mimics real world responses. Importantly, our cyclical generation framework also explores the space of new metasurface topologies. As an example application of the utility of our proposed architecture, we demonstrate the inverse design of gap-plasmon based half-wave plate metasurface for user-defined optical response. Our proposed technique can be easily generalized for designing nanophtonic metasurfaces for a wide range of targeted optical response.

scholarly journals Controllable inverse design of auxetic metamaterials using deep learning

2021 ◽  
pp. 110178
Author(s):  
Xiaoyang Zheng ◽  
Ta-Te Chen ◽  
Xiaofeng Guo ◽  
Sadaki Samitsu ◽  
Ikumu Watanabe
Keyword(s):  
Deep Learning ◽  
Inverse Design

scholarly journals Generative deep learning as a tool for inverse design of high entropy refractory alloys

2021 ◽  
Author(s):  
Arindam Debnath ◽  
Adam M. Krajewski ◽  
Hui Sun ◽  
Shuang Lin ◽  
Marcia Ahn ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inverse Design ◽  
High Entropy ◽  
Refractory Alloys

scholarly journals Establishing exhaustive metasurface robustness against fabrication uncertainties through deep learning

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ronald P. Jenkins ◽  
Sawyer D. Campbell ◽  
Douglas H. Werner
Keyword(s):  
Deep Learning ◽  
Design Process ◽  
Functional Materials ◽  
Computational Electromagnetics ◽  
Inverse Design ◽  
Design Tools ◽  
Structural Error ◽  
Significant Performance ◽  
Engineered Materials ◽  
Exhaustive Study

Abstract Photonic engineered materials have benefitted in recent years from exciting developments in computational electromagnetics and inverse-design tools. However, a commonly encountered issue is that highly performant and structurally complex functional materials found through inverse-design can lose significant performance upon being fabricated. This work introduces a method using deep learning (DL) to exhaustively analyze how structural issues affect the robustness of metasurface supercells, and we show how systems can be designed to guarantee significantly better performance. Moreover, we show that an exhaustive study of structural error is required to make strong guarantees about the performance of engineered materials. The introduction of DL into the inverse-design process makes this problem tractable, enabling optimization runtimes to be measurable in days rather than months and allowing designers to establish exhaustive metasurface robustness guarantees.

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