Far-field optical classification of subwavelength objects
Far-field optical classification of subwavelength objects
Object detection requires localizing and classifying the size and shape of an unknown object. Here we show that artificial-intelligence-enabled analysis of light scattered on objects that are not resolvable by conventional microscopy can be used for their shape classification. In a proof-of-principle experiment, we demonstrate classification with ∼90% accuracy for objects of unknown subwavelength dimensions in the range from λ/6 to λ/2 (where λ is the illumination wavelength) belonging to one of five shape classes. The method can be scaled to applications across the entire electromagnetic spectrum and used in a variety of tasks, such as the detection and study of biological particles, environmental sensing, and device diagnostics.
15380-15389
Kurdiumov, Sergei
ecd4ef31-4251-4703-bce6-2caee793b209
Papasimakis, Nikitas
f416bfa9-544c-4a3e-8a2d-bc1c11133a51
Ou, Bruce (Jun-Yu)
3fb703e3-b222-46d2-b4ee-75f296d9d64d
Zheludev, Nikolay
32fb6af7-97e4-4d11-bca6-805745e40cc6
7 April 2025
Kurdiumov, Sergei
ecd4ef31-4251-4703-bce6-2caee793b209
Papasimakis, Nikitas
f416bfa9-544c-4a3e-8a2d-bc1c11133a51
Ou, Bruce (Jun-Yu)
3fb703e3-b222-46d2-b4ee-75f296d9d64d
Zheludev, Nikolay
32fb6af7-97e4-4d11-bca6-805745e40cc6
Kurdiumov, Sergei, Papasimakis, Nikitas, Ou, Bruce (Jun-Yu) and Zheludev, Nikolay
(2025)
Far-field optical classification of subwavelength objects.
Optics Express, 33 (7), .
(doi:10.1364/OE.558631).
Abstract
Object detection requires localizing and classifying the size and shape of an unknown object. Here we show that artificial-intelligence-enabled analysis of light scattered on objects that are not resolvable by conventional microscopy can be used for their shape classification. In a proof-of-principle experiment, we demonstrate classification with ∼90% accuracy for objects of unknown subwavelength dimensions in the range from λ/6 to λ/2 (where λ is the illumination wavelength) belonging to one of five shape classes. The method can be scaled to applications across the entire electromagnetic spectrum and used in a variety of tasks, such as the detection and study of biological particles, environmental sensing, and device diagnostics.
This record has no associated files available for download.
More information
Accepted/In Press date: 14 March 2025
e-pub ahead of print date: 27 March 2025
Published date: 7 April 2025
Identifiers
Local EPrints ID: 501266
URI: http://eprints.soton.ac.uk/id/eprint/501266
ISSN: 1094-4087
PURE UUID: 658d4f4c-6af0-411c-98fe-ff28bc8ca572
Catalogue record
Date deposited: 28 May 2025 16:35
Last modified: 03 Sep 2025 01:44
Export record
Altmetrics
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
