Predicting Kerr Soliton Combs in Microresonators via Deep Neural Networks
Formation of the Kerr soliton combs is a widely recognized important but complex issue, which relates to cross-influences among intra-cavity nonlinearities, chromatic dispersions, mode interactions, and pumping effects. Here, we propose and demonstrate a deep neural network model to predict Kerr com...
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| Veröffentlicht in: | Journal of lightwave technology 2020-12, Vol.38 (23), p.6591-6599 |
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| container_end_page | 6599 |
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| container_issue | 23 |
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| container_title | Journal of lightwave technology |
| container_volume | 38 |
| creator | Tan, Teng Peng, Cheng Yuan, Zhongye Xie, Xu Liu, Hao Xie, Zhenda Huang, Shu-Wei Rao, Yunjiang Yao, Baicheng |
| description | Formation of the Kerr soliton combs is a widely recognized important but complex issue, which relates to cross-influences among intra-cavity nonlinearities, chromatic dispersions, mode interactions, and pumping effects. Here, we propose and demonstrate a deep neural network model to predict Kerr comb spectra in silica microspheres statistically, via training their transmission spectra. Such a scheme enables soliton comb identification under a particular pump scanning, with error |
| doi_str_mv | 10.1109/JLT.2020.3015586 |
| format | Article |
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Here, we propose and demonstrate a deep neural network model to predict Kerr comb spectra in silica microspheres statistically, via training their transmission spectra. Such a scheme enables soliton comb identification under a particular pump scanning, with error <8%, verified by experimental measurements. This study bridging the deep learning and the microcomb photonics, may provide a powerful and convenient tool for photonic device test and investigation.</description><identifier>ISSN: 0733-8724</identifier><identifier>EISSN: 1558-2213</identifier><identifier>DOI: 10.1109/JLT.2020.3015586</identifier><identifier>CODEN: JLTEDG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Deep neural networks ; Error analysis ; Kerr solitons ; Machine learning ; Microcavities ; microresonators ; Microspheres ; Neural networks ; Optical solitons ; Photonics ; Sensors ; Silicon dioxide ; Solitary waves ; Training</subject><ispartof>Journal of lightwave technology, 2020-12, Vol.38 (23), p.6591-6599</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-cc69386ae1d103622cb106e256ca4e4c69a1d35241ebda4ed2e60667bf138bc33</citedby><cites>FETCH-LOGICAL-c291t-cc69386ae1d103622cb106e256ca4e4c69a1d35241ebda4ed2e60667bf138bc33</cites><orcidid>0000-0001-8368-5815 ; 0000-0003-0717-5586 ; 0000-0003-2177-1536</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9165060$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9165060$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tan, Teng</creatorcontrib><creatorcontrib>Peng, Cheng</creatorcontrib><creatorcontrib>Yuan, Zhongye</creatorcontrib><creatorcontrib>Xie, Xu</creatorcontrib><creatorcontrib>Liu, Hao</creatorcontrib><creatorcontrib>Xie, Zhenda</creatorcontrib><creatorcontrib>Huang, Shu-Wei</creatorcontrib><creatorcontrib>Rao, Yunjiang</creatorcontrib><creatorcontrib>Yao, Baicheng</creatorcontrib><title>Predicting Kerr Soliton Combs in Microresonators via Deep Neural Networks</title><title>Journal of lightwave technology</title><addtitle>JLT</addtitle><description>Formation of the Kerr soliton combs is a widely recognized important but complex issue, which relates to cross-influences among intra-cavity nonlinearities, chromatic dispersions, mode interactions, and pumping effects. Here, we propose and demonstrate a deep neural network model to predict Kerr comb spectra in silica microspheres statistically, via training their transmission spectra. Such a scheme enables soliton comb identification under a particular pump scanning, with error <8%, verified by experimental measurements. This study bridging the deep learning and the microcomb photonics, may provide a powerful and convenient tool for photonic device test and investigation.</description><subject>Artificial neural networks</subject><subject>Deep neural networks</subject><subject>Error analysis</subject><subject>Kerr solitons</subject><subject>Machine learning</subject><subject>Microcavities</subject><subject>microresonators</subject><subject>Microspheres</subject><subject>Neural networks</subject><subject>Optical solitons</subject><subject>Photonics</subject><subject>Sensors</subject><subject>Silicon dioxide</subject><subject>Solitary waves</subject><subject>Training</subject><issn>0733-8724</issn><issn>1558-2213</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1LAzEQxYMoWKt3wUvA89bJxya7R6lf1foB1nPIZqeSWjc12Sr-96a0eHow897M40fIKYMRY1Bf3E9nIw4cRgJYWVZqjww2WnDOxD4ZgBaiqDSXh-QopQUAk7LSAzJ5idh61_vunT5gjPQ1LH0fOjoOn02ivqOP3sUQMYXO9iEm-u0tvUJc0SdcR7vM0v-E-JGOycHcLhOe7HRI3m6uZ-O7Yvp8OxlfTgvHa9YXzqlaVMoiaxkIxblrGCjkpXJWosxby1pRcsmwafOk5ahAKd3MmagaJ8SQnG_vrmL4WmPqzSKsY5dfGi5LXWuhpM4u2Lpy-ZQizs0q-k8bfw0DswFmMjCzAWZ2wHLkbBvxiPhvr5kqQYH4A-F_ZkY</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Tan, Teng</creator><creator>Peng, Cheng</creator><creator>Yuan, Zhongye</creator><creator>Xie, Xu</creator><creator>Liu, Hao</creator><creator>Xie, Zhenda</creator><creator>Huang, Shu-Wei</creator><creator>Rao, Yunjiang</creator><creator>Yao, Baicheng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Artificial neural networks Deep neural networks Error analysis Kerr solitons Machine learning Microcavities microresonators Microspheres Neural networks Optical solitons Photonics Sensors Silicon dioxide Solitary waves Training |
| title | Predicting Kerr Soliton Combs in Microresonators via Deep Neural Networks |
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