In-situ training in programmable photonic frequency circuits

authored by: Philip Rübeling, Oleksandr V. Marchukov, Filipe F. Bellotti, Ulrich B. Hoff, Nikolaj T. Zinner, Michael Kues
Abstract: Optical artificial neural networks (OANNs) leverage the advantages of photonic technologies including high processing speeds, low energy consumption, and mass production to establish a competitive and scalable platform for machine learning applications. While recent advancements have focused on harnessing spatial or temporal modes of light, the frequency domain attracts a lot of attention, with current implementations including spectral multiplexing, neural networks in nonlinear optical systems and extreme learning machines. Here, we present an experimental realization of a programmable photonic frequency circuit, realized with fiber-optical components, and implement the in-situ training with optical weight control of an OANN operating in the frequency domain. Input data is encoded into phases of frequency comb modes, and programmable phase and amplitude manipulations of the spectral modes enable in-situ training of the OANN, without employing a digital model of the device. The trained OANN achieves multiclass classification accuracies exceeding 90â€¯%, comparable to conventional machine learning approaches. This proof-of-concept demonstrates the feasibility of a multilayer OANN in the frequency domain and can be extended to a scalable, integrated photonic platform with ultrafast weights updates, with potential applications to single-shot classification in spectroscopy.
Organisation(s): Institute of Photonics
Hannover Centre for Optical Technologies (HOT)
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines
External Organisation(s): Kvantify APS
Aarhus University
Type: Article
Journal: Nanophotonics
Volume: 14
Pages: 2779-2786
No. of pages: 8
ISSN: 2192-8606
Publication date: 02.08.2025
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Biotechnology, Electronic, Optical and Magnetic Materials, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering
Sustainable Development Goals: SDG 7 - Affordable and Clean Energy
Electronic version(s): https://doi.org/10.1515/nanoph-2025-0125 (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{612f9f227348475582f345da6a7c1eb4,
title = "In-situ training in programmable photonic frequency circuits",
abstract = "Optical artificial neural networks (OANNs) leverage the advantages of photonic technologies including high processing speeds, low energy consumption, and mass production to establish a competitive and scalable platform for machine learning applications. While recent advancements have focused on harnessing spatial or temporal modes of light, the frequency domain attracts a lot of attention, with current implementations including spectral multiplexing, neural networks in nonlinear optical systems and extreme learning machines. Here, we present an experimental realization of a programmable photonic frequency circuit, realized with fiber-optical components, and implement the in-situ training with optical weight control of an OANN operating in the frequency domain. Input data is encoded into phases of frequency comb modes, and programmable phase and amplitude manipulations of the spectral modes enable in-situ training of the OANN, without employing a digital model of the device. The trained OANN achieves multiclass classification accuracies exceeding 90{\^a}€¯%, comparable to conventional machine learning approaches. This proof-of-concept demonstrates the feasibility of a multilayer OANN in the frequency domain and can be extended to a scalable, integrated photonic platform with ultrafast weights updates, with potential applications to single-shot classification in spectroscopy.",
keywords = "machine learning, photonic computing, ultrafast optics",
author = "Philip R{\"u}beling and Marchukov, {Oleksandr V.} and Bellotti, {Filipe F.} and Hoff, {Ulrich B.} and Zinner, {Nikolaj T.} and Michael Kues",
note = "Publisher Copyright: {\textcopyright} 2025 the author(s), published by De Gruyter, Berlin/Boston 2025.",
year = "2025",
month = aug,
day = "2",
doi = "10.1515/nanoph-2025-0125",
language = "English",
volume = "14",
pages = "2779--2786",
journal = "Nanophotonics",
issn = "2192-8606",
publisher = "Walter de Gruyter GmbH",
number = "16",
}

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