Design of a silicon Mach–Zehnder modulator via deep learning and evolutionary algorithms
As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performa...
Gespeichert in:
| Veröffentlicht in: | Scientific reports Jg. 13; H. 1; S. 14662 - 12 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Nature Publishing Group UK
05.09.2023
Nature Publishing Group Nature Portfolio |
| Schlagworte: | |
| ISSN: | 2045-2322, 2045-2322 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a
40
GHz
bandwidth and a driving voltage of
6.25
V
, or, alternatively,
47.5
GHz
with a driving voltage of
8
V
. Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs. |
|---|---|
| AbstractList | As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a
$$40~\text {GHz}$$
40
GHz
bandwidth and a driving voltage of
$$6.25~\text {V}$$
6.25
V
, or, alternatively,
$$47.5~\text {GHz}$$
47.5
GHz
with a driving voltage of
$$8~\text {V}$$
8
V
. Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs. As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a 40 GHz bandwidth and a driving voltage of 6.25 V , or, alternatively, 47.5 GHz with a driving voltage of 8 V . Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs. As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a 40GHz bandwidth and a driving voltage of 6.25V, or, alternatively, 47.5GHz with a driving voltage of 8V. Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs. Abstract As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a $$40~\text {GHz}$$ 40 GHz bandwidth and a driving voltage of $$6.25~\text {V}$$ 6.25 V , or, alternatively, $$47.5~\text {GHz}$$ 47.5 GHz with a driving voltage of $$8~\text {V}$$ 8 V . Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs. As an essential block in optical communication systems, silicon (Si) Mach-Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a [Formula: see text] bandwidth and a driving voltage of [Formula: see text], or, alternatively, [Formula: see text] with a driving voltage of [Formula: see text]. Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs.As an essential block in optical communication systems, silicon (Si) Mach-Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a [Formula: see text] bandwidth and a driving voltage of [Formula: see text], or, alternatively, [Formula: see text] with a driving voltage of [Formula: see text]. Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs. As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a $$40~\text {GHz}$$ 40GHz bandwidth and a driving voltage of $$6.25~\text {V}$$ 6.25V, or, alternatively, $$47.5~\text {GHz}$$ 47.5GHz with a driving voltage of $$8~\text {V}$$ 8V. Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs. |
| ArticleNumber | 14662 |
| Author | Aparecido de Paula, Romulo Sutili, Tiago Aldaya, Ivan Figueiredo, Rafael C. Bustamante, Yesica R. R. Pita, Julian L. |
| Author_xml | – sequence: 1 givenname: Romulo surname: Aparecido de Paula fullname: Aparecido de Paula, Romulo email: romulo.aparecido.22@ucl.ac.uk organization: Center for Advanced and Sustainable Technologies, State University of Sao Paulo (UNESP), Centre for Research and Development in Telecommunications (CPQD), Department of Electronic and Electrical Engineering, University College London (UCL) – sequence: 2 givenname: Ivan surname: Aldaya fullname: Aldaya, Ivan organization: Center for Advanced and Sustainable Technologies, State University of Sao Paulo (UNESP) – sequence: 3 givenname: Tiago surname: Sutili fullname: Sutili, Tiago organization: Centre for Research and Development in Telecommunications (CPQD) – sequence: 4 givenname: Rafael C. surname: Figueiredo fullname: Figueiredo, Rafael C. organization: Centre for Research and Development in Telecommunications (CPQD) – sequence: 5 givenname: Julian L. surname: Pita fullname: Pita, Julian L. organization: Department of Electrical Engineering, École de Technologie Supérieure (ÉTS) – sequence: 6 givenname: Yesica R. R. surname: Bustamante fullname: Bustamante, Yesica R. R. organization: Centre for Research and Development in Telecommunications (CPQD), Infinera Unipessoal Lda |
| BookMark | eNp9UsuOFCEUrZgxzjjOD7giceOmlAKKgpUx42uSMW50MxtCwaWaDg0tVHXizn_wD_0S6e4xOrMYWHAD55x7uDlPm5OYIjTN8w6_6jAVrwvreilaTGhbq1604lFzRjDrW0IJOfmvPm0uSlnjunoiWSefNKd04APGkp81N--g-Cmi5JBGxQdvUkSftVn9_vnrBlbRQkabZJeg55TRzmtkAbYogM7RxwnpaBHsUlhmn6LOP5AOU8p-Xm3Ks-ax06HAxe153nz78P7r5af2-svHq8u3163pOzG3xgAMbuicYbRuJrt-xL3AbsAwWG0lJ8zhUfJ-pII46aR1klKMjTWOO0zPm6ujrk16rbbZb6oPlbRXh4uUJ6Xz7E0AhQczGE5GiblhPcWjJo4Z0CNIC4x2VevNUWu7jBuwBuKcdbgjevcl-pWa0k51mAnM2N7Ny1uFnL4vUGa18cVACDpCWooignecUS5Jhb64B12nJcc6qwMK054zWVHiiDI5lZLBKeNnvR93NeBD7az2gVDHQKgaCHUIhBKVSu5R_37kQRI9kkoFxwnyP1cPsP4AtWzKiQ |
| CitedBy_id | crossref_primary_10_1002_lpor_202400624 crossref_primary_10_3390_photonics12080775 |
| Cites_doi | 10.1364/OPTICA.415762 10.1016/j.neucom.2017.04.075 10.1364/OL.42.000081 10.1109/JLT.2021.3066203 10.1016/j.yofte.2017.12.006 10.1364/OFC.2020.T3H.4 10.1063/1.5115136 10.1016/j.optlastec.2021.107376 10.1109/JLT.2014.2323954 10.1109/IMOC.2017.8121107 10.1109/JPROC.2018.2877636 10.1109/ICCV.2015.123 10.1109/JLT.2021.3074096 10.1364/OE.390315 10.1016/j.optlastec.2020.106844 10.48550/ARXIV.1505.00387 10.1016/S0378-4371(96)00271-3 10.3390/mi12060625 10.1109/CLEO.2008.4550982 10.48550/ARXIV.1502.03167 10.1364/OE.16.005218 10.1364/OPTICA.5.001354 10.1038/s41598-018-19171-x 10.1364/OE.23.014263 10.1109/LPT.2012.2191149 10.1002/9780470823941 10.1109/ICNN.1995.488968 10.1364/OFC.2016.Th4H.5 10.1049/cp.2019.1020 10.1364/FIO.2022.JTu4B.35 10.3390/photonics9010040 10.1109/CEC.2019.8790357 10.1364/PRJ.4.000153 10.1117/1.AP.3.2.024003 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023 The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023. Springer Nature Limited. Springer Nature Limited 2023 |
| Copyright_xml | – notice: The Author(s) 2023 – notice: The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2023. Springer Nature Limited. – notice: Springer Nature Limited 2023 |
| DBID | C6C AAYXX CITATION 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-023-41558-8 |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central ProQuest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 12 |
| ExternalDocumentID | oai_doaj_org_article_07c7c62b906c4530ba2f4ceabe9de431 PMC10480440 10_1038_s41598_023_41558_8 |
| GrantInformation_xml | – fundername: Fundação de Amparo à Pesquisa do Estado de São Paulo grantid: 2015/24517-8; 2015/24517-8 funderid: http://dx.doi.org/10.13039/501100001807 – fundername: Conselho Nacional de Desenvolvimento Científico e Tecnológico grantid: 432303/2018-9; 311035/2018-3; 305104/2021-7 funderid: http://dx.doi.org/10.13039/501100003593 – fundername: Sisfoton-MCTI Integration Laboratory grantid: 440220/2021-1 – fundername: Ministério da Ciência, Tecnologia, Inovações e Comunicações grantid: 01.19.0088.00 funderid: http://dx.doi.org/10.13039/501100011875 – fundername: ; grantid: 432303/2018-9; 311035/2018-3; 305104/2021-7 – fundername: ; grantid: 2015/24517-8; 2015/24517-8 – fundername: ; grantid: 440220/2021-1 – fundername: ; grantid: 01.19.0088.00 |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFFHD AFPKN CITATION PHGZM PHGZT PJZUB PPXIY PQGLB 7XB 8FK K9. PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c518t-ccee7f71fc434344915b0580f70e7dad9624f0b965b382f9f9df93300cdcf6f03 |
| IEDL.DBID | M2P |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001062861100006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2045-2322 |
| IngestDate | Mon Nov 10 04:34:15 EST 2025 Tue Nov 04 02:06:28 EST 2025 Thu Oct 02 10:22:59 EDT 2025 Tue Oct 07 09:10:38 EDT 2025 Sat Nov 29 06:05:09 EST 2025 Tue Nov 18 22:20:40 EST 2025 Fri Feb 21 02:40:02 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c518t-ccee7f71fc434344915b0580f70e7dad9624f0b965b382f9f9df93300cdcf6f03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/2861035649?pq-origsite=%requestingapplication% |
| PMID | 37670096 |
| PQID | 2861035649 |
| PQPubID | 2041939 |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_07c7c62b906c4530ba2f4ceabe9de431 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10480440 proquest_miscellaneous_2861643692 proquest_journals_2861035649 crossref_citationtrail_10_1038_s41598_023_41558_8 crossref_primary_10_1038_s41598_023_41558_8 springer_journals_10_1038_s41598_023_41558_8 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-09-05 |
| PublicationDateYYYYMMDD | 2023-09-05 |
| PublicationDate_xml | – month: 09 year: 2023 text: 2023-09-05 day: 05 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationYear | 2023 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Krause PerinJShastriAKahnJMData center links beyond 100 Gbit/s per wavelengthOpt. Fiber Technol.20184469852018OptFT..44...69K10.1016/j.yofte.2017.12.006Special Issue on Data Center Communications RahimATaking silicon photonics modulators to a higher performance level: State-of-the-art and a review of new technologiesAdv. Photonics2021312310.1117/1.AP.3.2.024003 DouradoDMde FariasGBGounellaRHde RochaLMCarmoJChallenges in silicon photonics modulators for data center interconnect applicationsOpt. Laser Technol.20211441073761:CAS:528:DC%2BB3MXhsFylsL7O10.1016/j.optlastec.2021.107376 ChenLDongPChenY-KChirp and dispersion tolerance of a single-drive push-pull silicon modulator at 28 Gb/sIEEE Photonics Technol. Lett.2012249369382012IPTL...24..936C1:CAS:528:DC%2BC38XhtFSru7nM10.1109/LPT.2012.2191149 He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. CoRR (2015). arXiv:1502.01852. KieningerCSilicon-organic hybrid (SOH) Mach–Zehnder modulators for 100 GBd PAM-4 signaling with sub-1 dB phase-shifter lossOpt. Express20202824693247072020OExpr..2824693K1:CAS:528:DC%2BB3cXitlSqt7rE10.1364/OE.39031532907004 VillarrubiaGDe PazJFChamosoPDe la PrietaFArtificial neural networks used in optimization problemsNeurocomputing2018272101610.1016/j.neucom.2017.04.075 Holland, J. H. Adaptation in Natural and Artificial Systems 2nd edn. (University of Michigan Press, 1975). ShuHSignificantly high modulation efficiency of compact graphene modulator based on silicon waveguideSci. Rep.201810.1038/s41598-018-19171-x304593236244417 Kennedy, J. & Eberhart, R. Particle swarm optimization. in Proceedings of ICNN’95—International Conference on Neural Networks, vol. 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968 (1995). SiewSYReview of silicon photonics technology and platform developmentJ. Lightwave Technol.202139437443892021JLwT...39.4374S1:CAS:528:DC%2BB3MXhvFaltrjL10.1109/JLT.2021.3066203 Islam, M. R., Lu, H. H., Hossain, M. J. & Li, L. A comparison of performance of GA, PSO and differential evolution algorithms for dynamic phase reconfiguration technology of a smart grid. in 2019 IEEE Congress on Evolutionary Computation (CEC), 858–865. https://doi.org/10.1109/CEC.2019.8790357 (2019). ZhouYModeling and optimization of a single-drive push-pull silicon Mach–Zehnder modulatorPhoton. Res.201641531611:CAS:528:DC%2BC1cXkvFKlsL0%3D10.1364/PRJ.4.000153 Jacques, M. et al. 200 Gbit/s net rate transmission over 2 km with a silicon photonic segmented MZM. in 45th European Conference on Optical Communication (ECOC 2019), 1–4, https://doi.org/10.1049/cp.2019.1020 (2019). ZhangMWangCKharelPZhuDLončarMIntegrated lithium niobate electro-optic modulators: When performance meets scalabilityOptica202186526672021Optic...8..652Z10.1364/OPTICA.415762 XuMMichelson interferometer modulator based on hybrid silicon and lithium niobate platformAPL Photonics201941008022019APLP....4j0802X1:CAS:528:DC%2BB3cXisVSltb8%3D10.1063/1.5115136 Romero-GarcíaSHigh-speed resonantly enhanced silicon photonics modulator with a large operating temperature rangeOpt. Lett.20174281842017OptL...42...81R10.1364/OL.42.00008128059183 Spector, S. J. et al. High-speed silicon electro-optical modulator that can be operated in carrier depletion or carrier injection mode. in 2008 Conference on Lasers and Electro-Optics and 2008 Conference on Quantum Electronics and Laser Science, 1–2, https://doi.org/10.1109/CLEO.2008.4550982 (2008). CST. Cst microwave studio advanced topics. Tech. Rep., CST-Computer Simulation Technology (2002). ZhouG-REffect of carrier lifetime on forward-biased silicon Mach–Zehnder modulatorsOpt. Express200816521852262008OExpr..16.5218Z10.1364/OE.16.00521818542624 Cisco annual internet report (2018–2023) (accessed 24 September 2021); https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html. SrivastavaNHintonGKrizhevskyASutskeverISalakhutdinovRDropout: A simple way to prevent neural networks from overfittingJ. Mach. Learn. Res.2014151929195832315921318.68153 Mulcahy, J., Peters, F. H. & Dai, X. Modulators in silicon photonics—heterogeneous integration & and beyond. Photonics. https://doi.org/10.3390/photonics9010040 (2022). DingRHigh-speed silicon modulator with slow-wave electrodes and fully independent differential driveJ. Lightwave Technol.201432224022472014JLwT...32.2240D1:CAS:528:DC%2BC2cXhtlGgsLnI10.1109/JLT.2014.2323954 Zhou, J., Wang, J. & Zhang, Q. Silicon photonics for 100 Gbaud. in Optical Fiber Communications Conference and Exhibition (OFC), 1–3 (2020). Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. https://doi.org/10.48550/ARXIV.1502.03167 (2015). Qing, A. Differential Evolution: Fundamentals and Applications in Electrical Engineering (Wiley-IEEE Press, 2009). Alpaydin, E. Introduction to Machine Learning. Adaptive Computation and Machine Learning 3rd edn. (MIT Press, 2014). Ansys. Lumerical. Tech. Rep., Lumerical Inc. (2003). AlamMSNet 220 Gbps/λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document} IM/DD transmission in O-band and C-band with silicon photonic traveling-wave MZMJ. Lightwave Technol.202139427042782021JLwT...39.4270A1:CAS:528:DC%2BB3MXhvFaltrjN10.1109/JLT.2021.3074096 de Paula, R. et al. Design of silicon Mach–Zehnder modulators employing deep neural networks. in Frontiers in Optics+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document}Laser Science 2022 (FIO, LS), JTu4B.35. https://doi.org/10.1364/FIO.2022.JTu4B.35 (Optica Publishing Group, 2022). TsallisCStarioloDAGeneralized simulated annealingPhys. A: Stat. Mech. Appl.199623339540610.1016/S0378-4371(96)00271-3 KimYHanJ-HAhnDKimSHeterogeneously-integrated optical phase shifters for next-generation modulators and switches on a silicon photonics platform: A reviewMicromachines202110.3390/mi12060625349453438708029 Srivastava, R. K., Greff, K. & Schmidhuber, J. Highway networks. https://doi.org/10.48550/ARXIV.1505.00387 (2015). Pathel, D. Design, Analysis, and Performance of a Silicon Photonic Traveling Wave Mach–Zehnder Modulator. Ph.D. Thesis, McGill University (2015). WitzensJHigh-speed silicon photonics modulatorsProc. IEEE2018106215821821:CAS:528:DC%2BC1MXhsFGlsbnI10.1109/JPROC.2018.2877636 PatelDDesign, analysis, and transmission system performance of a 41 GHz silicon photonic modulatorOpt. Express20152314263142872015OExpr..2314263P1:CAS:528:DC%2BC2sXks1WktLk%3D10.1364/OE.23.01426326072793 Xiao, X. et al. Substrate removed silicon Mach-Zehnder modulator for high baud rate optical intensity modulations. in Optical Fiber Communication Conference, Th4H.5. https://doi.org/10.1364/OFC.2016.Th4H.5 (Optical Society of America, 2016). Motta, D. A. et al. Design of a 40 GHz bandwidth slow-wave silicon modulator. in 2017 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC), 1–5, https://doi.org/10.1109/IMOC.2017.8121107 (2017). ChengQBahadoriMGlickMRumleySBergmanKRecent advances in optical technologies for data centers: A reviewOptica20185135413702018Optic...5.1354C1:CAS:528:DC%2BC1MXhtFSjtrrF10.1364/OPTICA.5.001354 MoshaevVLeibinYMalkaDOptimizations of Si PIN diode phase-shifter for controlling MZM quadrature bias point using SOI rib waveguide technologyOpt. Laser Technol.20211381068441:CAS:528:DC%2BB3MXhtlOnsL4%3D10.1016/j.optlastec.2020.106844 41558_CR1 41558_CR21 C Kieninger (41558_CR25) 2020; 28 41558_CR22 J Witzens (41558_CR23) 2018; 106 41558_CR41 V Moshaev (41558_CR16) 2021; 138 41558_CR20 C Tsallis (41558_CR40) 1996; 233 41558_CR29 H Shu (41558_CR26) 2018 41558_CR27 R Ding (41558_CR18) 2014; 32 41558_CR28 Y Kim (41558_CR9) 2021 A Rahim (41558_CR10) 2021; 3 G-R Zhou (41558_CR14) 2008; 16 MS Alam (41558_CR19) 2021; 39 G Villarrubia (41558_CR35) 2018; 272 41558_CR36 M Zhang (41558_CR5) 2021; 8 41558_CR15 41558_CR37 41558_CR12 41558_CR34 S Romero-García (41558_CR24) 2017; 42 41558_CR33 41558_CR30 41558_CR31 M Xu (41558_CR11) 2019; 4 D Patel (41558_CR17) 2015; 23 N Srivastava (41558_CR32) 2014; 15 41558_CR38 41558_CR39 SY Siew (41558_CR6) 2021; 39 J Krause Perin (41558_CR2) 2018; 44 Q Cheng (41558_CR3) 2018; 5 L Chen (41558_CR13) 2012; 24 41558_CR7 Y Zhou (41558_CR8) 2016; 4 DM Dourado (41558_CR4) 2021; 144 |
| References_xml | – reference: Islam, M. R., Lu, H. H., Hossain, M. J. & Li, L. A comparison of performance of GA, PSO and differential evolution algorithms for dynamic phase reconfiguration technology of a smart grid. in 2019 IEEE Congress on Evolutionary Computation (CEC), 858–865. https://doi.org/10.1109/CEC.2019.8790357 (2019). – reference: Motta, D. A. et al. Design of a 40 GHz bandwidth slow-wave silicon modulator. in 2017 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC), 1–5, https://doi.org/10.1109/IMOC.2017.8121107 (2017). – reference: Cisco annual internet report (2018–2023) (accessed 24 September 2021); https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html. – reference: ZhouG-REffect of carrier lifetime on forward-biased silicon Mach–Zehnder modulatorsOpt. Express200816521852262008OExpr..16.5218Z10.1364/OE.16.00521818542624 – reference: Ansys. Lumerical. Tech. Rep., Lumerical Inc. (2003). – reference: Qing, A. Differential Evolution: Fundamentals and Applications in Electrical Engineering (Wiley-IEEE Press, 2009). – reference: Mulcahy, J., Peters, F. H. & Dai, X. Modulators in silicon photonics—heterogeneous integration & and beyond. Photonics. https://doi.org/10.3390/photonics9010040 (2022). – reference: XuMMichelson interferometer modulator based on hybrid silicon and lithium niobate platformAPL Photonics201941008022019APLP....4j0802X1:CAS:528:DC%2BB3cXisVSltb8%3D10.1063/1.5115136 – reference: SiewSYReview of silicon photonics technology and platform developmentJ. Lightwave Technol.202139437443892021JLwT...39.4374S1:CAS:528:DC%2BB3MXhvFaltrjL10.1109/JLT.2021.3066203 – reference: Alpaydin, E. Introduction to Machine Learning. Adaptive Computation and Machine Learning 3rd edn. (MIT Press, 2014). – reference: Xiao, X. et al. Substrate removed silicon Mach-Zehnder modulator for high baud rate optical intensity modulations. in Optical Fiber Communication Conference, Th4H.5. https://doi.org/10.1364/OFC.2016.Th4H.5 (Optical Society of America, 2016). – reference: DouradoDMde FariasGBGounellaRHde RochaLMCarmoJChallenges in silicon photonics modulators for data center interconnect applicationsOpt. Laser Technol.20211441073761:CAS:528:DC%2BB3MXhsFylsL7O10.1016/j.optlastec.2021.107376 – reference: Kennedy, J. & Eberhart, R. Particle swarm optimization. in Proceedings of ICNN’95—International Conference on Neural Networks, vol. 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968 (1995). – reference: MoshaevVLeibinYMalkaDOptimizations of Si PIN diode phase-shifter for controlling MZM quadrature bias point using SOI rib waveguide technologyOpt. Laser Technol.20211381068441:CAS:528:DC%2BB3MXhtlOnsL4%3D10.1016/j.optlastec.2020.106844 – reference: RahimATaking silicon photonics modulators to a higher performance level: State-of-the-art and a review of new technologiesAdv. Photonics2021312310.1117/1.AP.3.2.024003 – reference: Holland, J. H. Adaptation in Natural and Artificial Systems 2nd edn. (University of Michigan Press, 1975). – reference: Zhou, J., Wang, J. & Zhang, Q. Silicon photonics for 100 Gbaud. in Optical Fiber Communications Conference and Exhibition (OFC), 1–3 (2020). – reference: Romero-GarcíaSHigh-speed resonantly enhanced silicon photonics modulator with a large operating temperature rangeOpt. Lett.20174281842017OptL...42...81R10.1364/OL.42.00008128059183 – reference: He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. CoRR (2015). arXiv:1502.01852. – reference: SrivastavaNHintonGKrizhevskyASutskeverISalakhutdinovRDropout: A simple way to prevent neural networks from overfittingJ. Mach. Learn. Res.2014151929195832315921318.68153 – reference: PatelDDesign, analysis, and transmission system performance of a 41 GHz silicon photonic modulatorOpt. Express20152314263142872015OExpr..2314263P1:CAS:528:DC%2BC2sXks1WktLk%3D10.1364/OE.23.01426326072793 – reference: ShuHSignificantly high modulation efficiency of compact graphene modulator based on silicon waveguideSci. Rep.201810.1038/s41598-018-19171-x304593236244417 – reference: CST. Cst microwave studio advanced topics. Tech. Rep., CST-Computer Simulation Technology (2002). – reference: Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. https://doi.org/10.48550/ARXIV.1502.03167 (2015). – reference: AlamMSNet 220 Gbps/λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document} IM/DD transmission in O-band and C-band with silicon photonic traveling-wave MZMJ. Lightwave Technol.202139427042782021JLwT...39.4270A1:CAS:528:DC%2BB3MXhvFaltrjN10.1109/JLT.2021.3074096 – reference: WitzensJHigh-speed silicon photonics modulatorsProc. IEEE2018106215821821:CAS:528:DC%2BC1MXhsFGlsbnI10.1109/JPROC.2018.2877636 – reference: Srivastava, R. K., Greff, K. & Schmidhuber, J. Highway networks. https://doi.org/10.48550/ARXIV.1505.00387 (2015). – reference: KimYHanJ-HAhnDKimSHeterogeneously-integrated optical phase shifters for next-generation modulators and switches on a silicon photonics platform: A reviewMicromachines202110.3390/mi12060625349453438708029 – reference: ZhouYModeling and optimization of a single-drive push-pull silicon Mach–Zehnder modulatorPhoton. Res.201641531611:CAS:528:DC%2BC1cXkvFKlsL0%3D10.1364/PRJ.4.000153 – reference: Spector, S. J. et al. High-speed silicon electro-optical modulator that can be operated in carrier depletion or carrier injection mode. in 2008 Conference on Lasers and Electro-Optics and 2008 Conference on Quantum Electronics and Laser Science, 1–2, https://doi.org/10.1109/CLEO.2008.4550982 (2008). – reference: ChengQBahadoriMGlickMRumleySBergmanKRecent advances in optical technologies for data centers: A reviewOptica20185135413702018Optic...5.1354C1:CAS:528:DC%2BC1MXhtFSjtrrF10.1364/OPTICA.5.001354 – reference: Krause PerinJShastriAKahnJMData center links beyond 100 Gbit/s per wavelengthOpt. Fiber Technol.20184469852018OptFT..44...69K10.1016/j.yofte.2017.12.006Special Issue on Data Center Communications – reference: de Paula, R. et al. Design of silicon Mach–Zehnder modulators employing deep neural networks. in Frontiers in Optics+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document}Laser Science 2022 (FIO, LS), JTu4B.35. https://doi.org/10.1364/FIO.2022.JTu4B.35 (Optica Publishing Group, 2022). – reference: KieningerCSilicon-organic hybrid (SOH) Mach–Zehnder modulators for 100 GBd PAM-4 signaling with sub-1 dB phase-shifter lossOpt. Express20202824693247072020OExpr..2824693K1:CAS:528:DC%2BB3cXitlSqt7rE10.1364/OE.39031532907004 – reference: Pathel, D. Design, Analysis, and Performance of a Silicon Photonic Traveling Wave Mach–Zehnder Modulator. Ph.D. Thesis, McGill University (2015). – reference: Jacques, M. et al. 200 Gbit/s net rate transmission over 2 km with a silicon photonic segmented MZM. in 45th European Conference on Optical Communication (ECOC 2019), 1–4, https://doi.org/10.1049/cp.2019.1020 (2019). – reference: DingRHigh-speed silicon modulator with slow-wave electrodes and fully independent differential driveJ. Lightwave Technol.201432224022472014JLwT...32.2240D1:CAS:528:DC%2BC2cXhtlGgsLnI10.1109/JLT.2014.2323954 – reference: VillarrubiaGDe PazJFChamosoPDe la PrietaFArtificial neural networks used in optimization problemsNeurocomputing2018272101610.1016/j.neucom.2017.04.075 – reference: ChenLDongPChenY-KChirp and dispersion tolerance of a single-drive push-pull silicon modulator at 28 Gb/sIEEE Photonics Technol. Lett.2012249369382012IPTL...24..936C1:CAS:528:DC%2BC38XhtFSru7nM10.1109/LPT.2012.2191149 – reference: TsallisCStarioloDAGeneralized simulated annealingPhys. A: Stat. Mech. Appl.199623339540610.1016/S0378-4371(96)00271-3 – reference: ZhangMWangCKharelPZhuDLončarMIntegrated lithium niobate electro-optic modulators: When performance meets scalabilityOptica202186526672021Optic...8..652Z10.1364/OPTICA.415762 – volume: 8 start-page: 652 year: 2021 ident: 41558_CR5 publication-title: Optica doi: 10.1364/OPTICA.415762 – volume: 272 start-page: 10 year: 2018 ident: 41558_CR35 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.04.075 – volume: 42 start-page: 81 year: 2017 ident: 41558_CR24 publication-title: Opt. Lett. doi: 10.1364/OL.42.000081 – volume: 39 start-page: 4374 year: 2021 ident: 41558_CR6 publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2021.3066203 – volume: 44 start-page: 69 year: 2018 ident: 41558_CR2 publication-title: Opt. Fiber Technol. doi: 10.1016/j.yofte.2017.12.006 – ident: 41558_CR20 doi: 10.1364/OFC.2020.T3H.4 – volume: 4 start-page: 100802 year: 2019 ident: 41558_CR11 publication-title: APL Photonics doi: 10.1063/1.5115136 – volume: 144 start-page: 107376 year: 2021 ident: 41558_CR4 publication-title: Opt. Laser Technol. doi: 10.1016/j.optlastec.2021.107376 – ident: 41558_CR28 – volume: 32 start-page: 2240 year: 2014 ident: 41558_CR18 publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2014.2323954 – ident: 41558_CR12 – ident: 41558_CR29 doi: 10.1109/IMOC.2017.8121107 – volume: 106 start-page: 2158 year: 2018 ident: 41558_CR23 publication-title: Proc. IEEE doi: 10.1109/JPROC.2018.2877636 – ident: 41558_CR31 doi: 10.1109/ICCV.2015.123 – volume: 39 start-page: 4270 year: 2021 ident: 41558_CR19 publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2021.3074096 – volume: 28 start-page: 24693 year: 2020 ident: 41558_CR25 publication-title: Opt. Express doi: 10.1364/OE.390315 – volume: 138 start-page: 106844 year: 2021 ident: 41558_CR16 publication-title: Opt. Laser Technol. doi: 10.1016/j.optlastec.2020.106844 – ident: 41558_CR34 doi: 10.48550/ARXIV.1505.00387 – volume: 233 start-page: 395 year: 1996 ident: 41558_CR40 publication-title: Phys. A: Stat. Mech. Appl. doi: 10.1016/S0378-4371(96)00271-3 – year: 2021 ident: 41558_CR9 publication-title: Micromachines doi: 10.3390/mi12060625 – volume: 15 start-page: 1929 year: 2014 ident: 41558_CR32 publication-title: J. Mach. Learn. Res. – ident: 41558_CR15 doi: 10.1109/CLEO.2008.4550982 – ident: 41558_CR33 doi: 10.48550/ARXIV.1502.03167 – volume: 16 start-page: 5218 year: 2008 ident: 41558_CR14 publication-title: Opt. Express doi: 10.1364/OE.16.005218 – volume: 5 start-page: 1354 year: 2018 ident: 41558_CR3 publication-title: Optica doi: 10.1364/OPTICA.5.001354 – year: 2018 ident: 41558_CR26 publication-title: Sci. Rep. doi: 10.1038/s41598-018-19171-x – volume: 23 start-page: 14263 year: 2015 ident: 41558_CR17 publication-title: Opt. Express doi: 10.1364/OE.23.014263 – ident: 41558_CR27 – volume: 24 start-page: 936 year: 2012 ident: 41558_CR13 publication-title: IEEE Photonics Technol. Lett. doi: 10.1109/LPT.2012.2191149 – ident: 41558_CR36 doi: 10.1002/9780470823941 – ident: 41558_CR39 doi: 10.1109/ICNN.1995.488968 – ident: 41558_CR1 – ident: 41558_CR21 doi: 10.1364/OFC.2016.Th4H.5 – ident: 41558_CR22 doi: 10.1049/cp.2019.1020 – ident: 41558_CR30 – ident: 41558_CR37 doi: 10.1364/FIO.2022.JTu4B.35 – ident: 41558_CR7 doi: 10.3390/photonics9010040 – ident: 41558_CR41 doi: 10.1109/CEC.2019.8790357 – volume: 4 start-page: 153 year: 2016 ident: 41558_CR8 publication-title: Photon. Res. doi: 10.1364/PRJ.4.000153 – ident: 41558_CR38 – volume: 3 start-page: 1 year: 2021 ident: 41558_CR10 publication-title: Adv. Photonics doi: 10.1117/1.AP.3.2.024003 |
| SSID | ssj0000529419 |
| Score | 2.4487312 |
| Snippet | As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for... As an essential block in optical communication systems, silicon (Si) Mach-Zehnder modulators (MZMs) are approaching the limits of possible performance for... Abstract As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance... |
| SourceID | doaj pubmedcentral proquest crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 14662 |
| SubjectTerms | 639/166 639/166/987 639/624 639/624/1075/1079 639/624/1075/401 Algorithms Communications systems Deep learning Design Humanities and Social Sciences multidisciplinary Neural networks Optimization Problem solving Science Science (multidisciplinary) Silicon Simulation Voltage |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQBRIXxFOkFGQkbmDV8SO2j-VRcYCKA6CqF8vxoxtpm1TJdqXe-A_8w_4SbCe7NJWAC8fEtmLPfKNxPJ5vAHgViDfKOokoNQEx7kpkopdGlhDLfXxW-cDt-ydxdCSPj9WXa6W-0p2wkR54FNw-FlbYitQKV5ZximtDArPe1F45z3IGNcFCXfuZGlm9iWKlmrJkMJX7Q_RUKZuMUJR8qERy5okyYf9sl3nzjuSNQGn2P4f3wb1p4wgPxgk_ALd8-xDcGUtJXj4CJ-_zVQzYBWjg0Cyjglv42djF1Y-fJ36RUljgWedSsa6uh-vGQOf9OZyKRpxC0zro1xMOTX8JzfK065vV4mx4DL4dfvj67iOa6iYgy0u5QjY6PhFEGWxKG2VMlbzGXOIgsBfOOFURFnCtKl5TSYIKyoV0roGts6EKmD4BO23X-qcAGpP4ChPFfSWZYbwOXJZMSFxzx6gjBSg3MtR2IhVPtS2WOge3qdSj3HWUu85y17IAr7djzkdKjb_2fptUs-2Z6LDziwgSPYFE_wskBdjbKFZPNjpoIiMoKa-YKsDLbXO0rhQyMa3vLsY-cc9WqbhSOQPEbELzlrZZZJ7uMuXrM4YL8GaDnd9f__OKd__Hip-BuyRhPYW--B7YWfUX_jm4bderZuhfZGP5BbHvGSs priority: 102 providerName: Directory of Open Access Journals |
| Title | Design of a silicon Mach–Zehnder modulator via deep learning and evolutionary algorithms |
| URI | https://link.springer.com/article/10.1038/s41598-023-41558-8 https://www.proquest.com/docview/2861035649 https://www.proquest.com/docview/2861643692 https://pubmed.ncbi.nlm.nih.gov/PMC10480440 https://doaj.org/article/07c7c62b906c4530ba2f4ceabe9de431 |
| Volume | 13 |
| WOSCitedRecordID | wos001062861100006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR1Nb9Mw1GIrSFz4niiMykjcIJrj2LF9Qgw2gUSrCAEqu0SOP9pKXVKartJu_Af-Ib8E23U7ZRK7cLGU2Eme897ze3qfALyy2EihNE-yTNqEUJ0m0knpRGGsqHHXIhjcvn9moxEfj0URDW5tDKvcnonhoNaN8jbyI8zdKzKaE_F28TPxXaO8dzW20NgDPafZpD6ka4iLnY3Fe7FIKmKuDMr4Uevklc8pw1niJSlPeEcehbL9HV3zeqTkNXdpkEKn9_8X_gfgXtQ_4bsNwTwEt0z9CNzZdKS8fAzOPoSIDthYKGE7mzs6qeFQqumfX7_PzNRnwsDzRvueX80SrmcSamMWMPaemEBZa2jWkZzl8hLK-cRBsZqet0_At9OTr-8_JrH9QqLcr1wlyslPZllqlc8-JUSktEKUI8uQYVpqkWNiUSVyWmUcW2GFtt48gpRWNrcoOwD7dVObpwBK6cse-kr5OSeS0MpSnhLGUUU1yTTug3SLhFLF2uS-Rca8DD7yjJcbxJUOcWVAXMn74PXumcWmMseNq489bncrfVXtcKNZTsrIpCViiqkcVwLlitAMVRJbooysjNDGaVp9cLjFbRlZvS2vENsHL3fTjkm950XWprnYrHGqXy7cTnmHojoAdWfq2TSU-0592j8hqA_ebInv6uv_3vGzm4F9Du5izwbeN0YPwf5qeWFegNtqvZq1ywHYY2MWRj4AveOTUfFlEMwVg8BhfmRu7BWfhsWPv-i4LYM |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFLVKAcGGNyJQwEiwgqiOYyf2AiGgVK06HXVRUNVN6vgxM9I0GSbTQbPjH_gPPoovwTePqaYS3XXBMonjxPG59zq-j4PQa0etktqIMI6VCxk3Uai8lQ41pZpbfyzrDbdvvbTfF0dH8mAN_e5yYSCsstOJtaI2pYY98k0qfBcxT5j8MPkeAmsUeFc7Co0GFnt28cP_slXvd7f8_L6hdPvL4eedsGUVCDWPxCzU3iykLo2chqRKxmTEc8IFcSmxqVFGJpQ5ksuE57GgTjppHPz1E220SxyJfb_X0HUGlcUgVJAeLPd0wGvGItnm5pBYbFbePkIOG41DsNwiFCv2r6YJWFnbXozMvOCera3e9t3_7XvdQ3fa9TX-2AjEfbRmiwfoZsO4uXiIjrfqiBVcOqxwNRp7OSjwvtLDPz9_HdshZPrg09IAp1k5xfORwsbaCW65NQZYFQbbeSuuarrAajzwo54NT6tH6OuVDOwxWi_Kwj5BWCko6whMAIlgivHccRGxVJCcGxYbGqCom_RMt7XXgQJknNUxALHIGqBkHihZDZRMBOjt8p5JU3nk0tafAEvLllA1vD5RTgdZq4QykupUJzSXJNGMxyRX1DFtVW6lsX4lGaCNDktZq8qq7BxIAXq1vOyVEHiWVGHLs6aNX9om0o9UrCB45YVWrxSjYV3OPIKyBl6cAvSuA_v50_894qeXv-xLdGvncL-X9Xb7e8_QbQoiCH5AvoHWZ9Mz-xzd0PPZqJq-qGUYo5OrFoK_zT6D5A |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9MwHLdGB2gX3ojCACPBCaI6jp3YB4SAUlFtq3oANHbxHD_aSl3SNV1Rb3wHvg0fh0-CnUenTmK3HTgmcR5Ofv9H_H_8AHhpsZFcaRZEkbQBoToMpLPSgcJYUeO2ebng9m0_GQzY4SEfboHfTS2MT6tsdGKpqHWu_Bp5BzN3iYjGhHdsnRYx7PbezU4DzyDlI60NnUYFkT2z-uF-34q3_a771q8w7n368vFzUDMMBIqGbBEoZyISm4RW-QJLQnhIU0QZsgkyiZaax5hYlPKYphHDlluurV8BQEorG1sUueteA9vOJSe4BbaH_YPh9_UKj4-hkZDXlTooYp3CWUtf0YajwNtxFrANa1iSBmx4uhfzNC8Ea0sb2Lv9P7-9O-BW7XnD95Wo3AVbJrsHblRcnKv74Khb5rLA3EIJi8nUSUgGD6Qa__n568iMfQ0QPMm1ZzvL53A5kVAbM4M168YIykxDs6wFWc5XUE5HbtaL8UnxAHy9kok9BK0sz8wjAKX0DR89R0DMiCQ0tZSFJGEopZpEGrdB2ABAqLoruycHmYoyOyBiogKNcKARJWgEa4PX63NmVU-SS0d_8Lhaj_T9xMsd-XwkavUkUKISFeOUo1gRGqFUYkuUkanh2jgfsw12G1yJWskV4hxUbfBifdipJx9zkpnJz6oxzumNuZsp20DzxgNtHskm47LReegbHhCC2uBNA_zzu_97xo8vf9jn4KbDvtjvD_aegB3spdEHCOkuaC3mZ-YpuK6Wi0kxf1YLNATHVy0FfwHc2I4t |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Design+of+a+silicon+Mach%E2%80%93Zehnder+modulator+via+deep+learning+and+evolutionary+algorithms&rft.jtitle=Scientific+reports&rft.au=Aparecido+de+Paula%2C+Romulo&rft.au=Aldaya%2C+Ivan&rft.au=Sutili%2C+Tiago&rft.au=Figueiredo%2C+Rafael+C.&rft.date=2023-09-05&rft.issn=2045-2322&rft.eissn=2045-2322&rft.volume=13&rft.issue=1&rft_id=info:doi/10.1038%2Fs41598-023-41558-8&rft.externalDBID=n%2Fa&rft.externalDocID=10_1038_s41598_023_41558_8 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |