Deep Learning Assisted Inverse Design and Functioning of Nanophotonic Devices
A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2181
Special Issue Editors
Interests: optical force; light scattering; optical manipulation
Special Issue Information
Dear Colleagues,
Nanoscale photonic devices have received significant attention due to their compactness and remarkable functionality. Restricted by the computational and fabrication costs, prediction of the structural design is a challenge. For a great number of optimization parameters available for a complex nanophotonic device, the inverse design is preferable in comparison to iterative brute-force approaches. The inverse design as a tool for finding an appropriate nanostructure with desired functionality can be realized on different platforms, including topological optimization and machine learning. The latter technique is extremely promising since the training of artificial neuron networks is performed once significantly saving computational resources. This Special Issue welcomes researchers in the fields of computational physics, photonic engineering, and nanoscale optics to contribute research articles of any format, including review papers. The topic of requested research papers is devoted, but not limited to the application of the deep learning and inverse design approaches to improve performance of photonic nanostructures exploited in dispersion and modal coupling engineering, solar energy harvesting and optomechanics, non-Hermitian and topological photonics, etc.
Dr. Dongliang Gao
Prof. Dr. Andrey V. Novitsky
Guest Editors
Manuscript Submission Information
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Keywords
- nanophotonics
- deep learning
- inverse design
- structural optimization
- neuron network
