Maschinelles Lernen kombiniert mit Nanooptik als zukunftsweisende Technologie für Charakterisierung und Informationsverarbeitung auf der Nanoskala
Optik, Quantenoptik und Physik der Atome, Moleküle und Plasmen
Theoretische Physik der kondensierten Materie
Zusammenfassung der Projektergebnisse
Despite their ability to solve complicated mathematical problems, classical computational techniques are pretty bad at other tasks, which humans usually solve without any difficulty. Such problems include for instance image or speech recognition. During the last decade, great progress has been made in the field of artificial neural networks (ANNs) – computational models inspired by how the human brain works. ANNs can be trained to categorize such problems and to eventually solve them very efficiently. The goal of this DFG research project is to apply deep artificial neural networks to the field of nano-optics. In a first step, ANNs will be used for the prediction of optical properties of photonic nano-structures and meta-surfaces. In preliminary studies that I have done in preparation of this proposal, I have demonstrated the capability of neural networks for the ultra-rapid prediction of the optical scattering of complex photonic nanostructures. By training ANNs with experimental datasets, I will obtain fully phenomenological models for the prediction of optical effects. In a second work package, deep learning techniques will be applied to the design of nano-optical devices by solving “inverse” problems – the prediction of nanostructure geometries which offer a desired optical response. Next to the conception of individual nano-optical components, I will explore the use of deep learning methods in complex, multi-modal systems such as speckle-based compressive sensing or all-optical reconfigurable photonic routing and for optical information encoding.
Projektbezogene Publikationen (Auswahl)
- Optics Express 27, 20965–20979, (2019) “Deep Learning Enabled Real Time Speckle Recognition and Hyperspectral Imaging Using a Multimode Fiber Array”
U. Kürüm, P. R. Wiecha, R. French, and O. L. Muskens
(Siehe online unter https://doi.org/10.1364/OE.27.020965) - Optics Express 27, 29069–29081, (2019) “Design of Plasmonic Directional Antennas via Evolutionary Optimization”
P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, and A. Arbouet
(Siehe online unter https://doi.org/10.1364/OE.27.029069) - Nano Letters 20, 329–338, (2020) “Deep Learning Meets Nanophotonics”
P. R. Wiecha and O. L. Muskens
(Siehe online unter https://doi.org/10.1021/acs.nanolett.9b03971)
