In-situ training in programmable photonic frequency circuits
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 harn...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-06-01
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| Series: | Nanophotonics |
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| Online Access: | https://doi.org/10.1515/nanoph-2025-0125 |
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| author | Rübeling Philip Marchukov Oleksandr V. Bellotti Filipe F. Hoff Ulrich B. Zinner Nikolaj T. Kues Michael |
| author_facet | Rübeling Philip Marchukov Oleksandr V. Bellotti Filipe F. Hoff Ulrich B. Zinner Nikolaj T. Kues Michael |
| author_sort | Rübeling Philip |
| collection | DOAJ |
| description | 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 %, 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. |
| format | Article |
| id | doaj-art-552da5922f7a475bafcb4059d6cee709 |
| institution | Kabale University |
| issn | 2192-8614 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj-art-552da5922f7a475bafcb4059d6cee7092025-08-20T04:03:17ZengDe GruyterNanophotonics2192-86142025-06-0114162779278610.1515/nanoph-2025-0125In-situ training in programmable photonic frequency circuitsRübeling Philip0Marchukov Oleksandr V.1Bellotti Filipe F.2Hoff Ulrich B.3Zinner Nikolaj T.4Kues Michael5Institute of Photonics (IOP), Leibniz University Hannover, Nienburger Str. 17, Hannover, GermanyInstitute of Photonics (IOP), Leibniz University Hannover, Nienburger Str. 17, Hannover, GermanyKvantify Aps, DK-2100Copenhagen, DenmarkKvantify Aps, DK-2100Copenhagen, DenmarkKvantify Aps, DK-2100Copenhagen, DenmarkInstitute of Photonics (IOP), Leibniz University Hannover, Nienburger Str. 17, Hannover, GermanyOptical 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 %, 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.https://doi.org/10.1515/nanoph-2025-0125machine learningphotonic computingultrafast optics |
| spellingShingle | Rübeling Philip Marchukov Oleksandr V. Bellotti Filipe F. Hoff Ulrich B. Zinner Nikolaj T. Kues Michael In-situ training in programmable photonic frequency circuits Nanophotonics machine learning photonic computing ultrafast optics |
| title | In-situ training in programmable photonic frequency circuits |
| title_full | In-situ training in programmable photonic frequency circuits |
| title_fullStr | In-situ training in programmable photonic frequency circuits |
| title_full_unstemmed | In-situ training in programmable photonic frequency circuits |
| title_short | In-situ training in programmable photonic frequency circuits |
| title_sort | in situ training in programmable photonic frequency circuits |
| topic | machine learning photonic computing ultrafast optics |
| url | https://doi.org/10.1515/nanoph-2025-0125 |
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