AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets
Recent advances in silicon photonics promise to revolutionize modern technology by improving the performance of everyday devices in multiple fields (Thomson et al 2016 J. Opt. 18 073003). However, as the industry moves into a mass fabrication phase, the problem of adequate testing of integrated sili...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adaa4d |
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| author | Jaime Gonzalo Flor Flores Jim Solomon Connor Nasseraddin Talha Yerebakan Andrey B Matsko Chee Wei Wong |
| author_facet | Jaime Gonzalo Flor Flores Jim Solomon Connor Nasseraddin Talha Yerebakan Andrey B Matsko Chee Wei Wong |
| author_sort | Jaime Gonzalo Flor Flores |
| collection | DOAJ |
| description | Recent advances in silicon photonics promise to revolutionize modern technology by improving the performance of everyday devices in multiple fields (Thomson et al 2016 J. Opt. 18 073003). However, as the industry moves into a mass fabrication phase, the problem of adequate testing of integrated silicon photonics devices remains to be solved. A cost-efficient manner that reduces schedule risk needs to involve automated testing of multiple devices that share common characteristics such as input–output coupling mechanisms, but at the same time needs to be generalizable to various types of devices and scenarios. This paper presents a neural network-based automated system designed for in-plane fiber-chip-fiber testing, characterization, and active alignment of silicon photonic devices that use process-design-kit library edge couplers. The presented approach combines state-of-the-art computer vision techniques with time-series analysis, to control a testing setup that can process multiple devices and be quickly tuned to incorporate additional hardware. The system can operate at vacuum or atmospheric pressures and maintains stability for fairly long time periods in excess of a month. |
| format | Article |
| id | doaj-art-3349ccc0a14d41978b42ad635327c932 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-3349ccc0a14d41978b42ad635327c9322025-01-29T11:21:29ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101501810.1088/2632-2153/adaa4dAtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chipletsJaime Gonzalo Flor Flores0Jim Solomon1Connor Nasseraddin2https://orcid.org/0009-0002-0688-8728Talha Yerebakan3https://orcid.org/0009-0007-8667-7651Andrey B Matsko4Chee Wei Wong5https://orcid.org/0000-0001-7652-7720Fang Lu Mesoscopic Optics and Quantum Electronics Laboratory, University of California , Los Angeles, CA 90095, United States of AmericaFang Lu Mesoscopic Optics and Quantum Electronics Laboratory, University of California , Los Angeles, CA 90095, United States of AmericaFang Lu Mesoscopic Optics and Quantum Electronics Laboratory, University of California , Los Angeles, CA 90095, United States of AmericaFang Lu Mesoscopic Optics and Quantum Electronics Laboratory, University of California , Los Angeles, CA 90095, United States of AmericaNASA, Jet Propulsion Laboratory (JPL) , Pasadena, CA 91109, United States of AmericaFang Lu Mesoscopic Optics and Quantum Electronics Laboratory, University of California , Los Angeles, CA 90095, United States of AmericaRecent advances in silicon photonics promise to revolutionize modern technology by improving the performance of everyday devices in multiple fields (Thomson et al 2016 J. Opt. 18 073003). However, as the industry moves into a mass fabrication phase, the problem of adequate testing of integrated silicon photonics devices remains to be solved. A cost-efficient manner that reduces schedule risk needs to involve automated testing of multiple devices that share common characteristics such as input–output coupling mechanisms, but at the same time needs to be generalizable to various types of devices and scenarios. This paper presents a neural network-based automated system designed for in-plane fiber-chip-fiber testing, characterization, and active alignment of silicon photonic devices that use process-design-kit library edge couplers. The presented approach combines state-of-the-art computer vision techniques with time-series analysis, to control a testing setup that can process multiple devices and be quickly tuned to incorporate additional hardware. The system can operate at vacuum or atmospheric pressures and maintains stability for fairly long time periods in excess of a month.https://doi.org/10.1088/2632-2153/adaa4doptomechanical accelerometercavity optomechanicstestingautomatic couplingactive alignmentsilicon photonics |
| spellingShingle | Jaime Gonzalo Flor Flores Jim Solomon Connor Nasseraddin Talha Yerebakan Andrey B Matsko Chee Wei Wong AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets Machine Learning: Science and Technology optomechanical accelerometer cavity optomechanics testing automatic coupling active alignment silicon photonics |
| title | AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets |
| title_full | AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets |
| title_fullStr | AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets |
| title_full_unstemmed | AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets |
| title_short | AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets |
| title_sort | atomics a deep learning based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets |
| topic | optomechanical accelerometer cavity optomechanics testing automatic coupling active alignment silicon photonics |
| url | https://doi.org/10.1088/2632-2153/adaa4d |
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