Deep Learning and Machine Learning Based Highly Accurate Prediction of Reflection for Multi Layers Anti-Reflection Coatings

Deep Learning and Machine Learning Based Highly Accurate Prediction of Reflection for Multi Layers Anti-Reflection Coatings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Deep Learning and Machine Learning Based Highly Accurate Prediction of Reflection for Multi Layers Anti-Reflection Coatings Semih OKTAY, İremnur DURU, Halit BAKIR, Timuçin Emre TABARU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4812441/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Optical and Quantum Electronics → Version 1 posted 13 You are reading this latest preprint version Abstract Optical filters are specialized structures designed to selectively transmit specific regions of the optical spectrum while blocking others. These filters achieve their desired properties using a variety of materials and methods. This work focuses on designing and optimizing multilayer optical filters utilizing Machine Learning (ML) and Deep Learning (DL) techniques. A dataset is created from Finite Difference Time Domain (FDTD) simulations of Germanium (Ge) substrates coated with alumina (Al 2 O 3 ) or silica (SiO 2 ). The dataset consists of bands 3–5, typical for medium-wave infrared (MWIR) and long-wave infrared (LWIR) bands, and includes reflectance values for wavelengths varying between 3 µm and 12 µm. Six ML algorithms and a DL model, including artificial neural networks (ANN) and convolutional neural networks (CNN), are evaluated to determine the most effective approach for predicting reflectance properties. Bayesian optimization is used to fine-tune the hyperparameters of the DL model, achieving optimum performance. The results show that ML models, particularly decision tree, random forest, and bagging methods, outperform the DL model in predicting reflectance values and provide a valuable reference for designing and fabricating optical thin-film filters. Anti-reflection coating Machine learning Deep learning Hyperparameter Optimization Infrared region Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Optical and Quantum Electronics → Version 1 posted Editorial decision: Revision requested 10 Oct, 2024 Reviews received at journal 09 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviews received at journal 01 Oct, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviewers agreed at journal 19 Sep, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers invited by journal 21 Aug, 2024 Editor assigned by journal 27 Jul, 2024 Submission checks completed at journal 27 Jul, 2024 First submitted to journal 27 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4812441","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343274148,"identity":"70647f88-f5b5-4626-8bc2-fc2fe9bc2ef3","order_by":0,"name":"Semih OKTAY","email":"","orcid":"","institution":"Sivas University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Semih","middleName":"","lastName":"OKTAY","suffix":""},{"id":343274149,"identity":"1d519e10-4848-4169-8b44-4571252260fd","order_by":1,"name":"İremnur DURU","email":"","orcid":"","institution":"Sivas University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"İremnur","middleName":"","lastName":"DURU","suffix":""},{"id":343274150,"identity":"7cff8a22-7ea5-4bd8-b1f9-8ef5e407a3e3","order_by":2,"name":"Halit BAKIR","email":"","orcid":"","institution":"Sivas University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Halit","middleName":"","lastName":"BAKIR","suffix":""},{"id":343274151,"identity":"6160fcde-843b-429f-882a-801e70595ae5","order_by":3,"name":"Timuçin Emre TABARU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYHACNhAhx8AM4TE2EKEBrMUYSQszcVoSYYYT1sIv3/zswc89h9Pnt/MYfvzBYCO74QD/sQ/4tEi2sZkb9jw7nLvhMI+xNA9DmvGGA8zMM/BpMTjGYCbBcwCohZktQZqB4XAiSAteh9kfY/8m+efA4XT5Zrbknz8Y/hPWYsDGYyYNtCWB4TDzMQkehgOEtUgcyyk3ljmQbrgBqMWaxyDZeOZhZmO8Wvibj297+OaAtbx8/8Hmmz8q7GT7jjc+xqsF3Z1ATCgmR8EoGAWjYBQQBgAelUSk70O6AAAAAABJRU5ErkJggg==","orcid":"","institution":"Sivas University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Timuçin","middleName":"Emre","lastName":"TABARU","suffix":""}],"badges":[],"createdAt":"2024-07-27 10:02:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4812441/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4812441/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11082-024-08006-x","type":"published","date":"2025-01-11T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73693952,"identity":"9f2ec27b-df54-41c9-b5e8-264485202df5","added_by":"auto","created_at":"2025-01-13 16:09:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":561181,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscripttetOQE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4812441/v1_covered_07b31b09-6b39-451f-8cfc-60f5db2ae9df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning and Machine Learning Based Highly Accurate Prediction of Reflection for Multi Layers Anti-Reflection Coatings","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"optical-and-quantum-electronics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oqel","sideBox":"Learn more about [Optical and Quantum Electronics](https://www.springer.com/journal/11082)","snPcode":"11082","submissionUrl":"https://submission.nature.com/new-submission/11082/3","title":"Optical and Quantum Electronics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Anti-reflection coating, Machine learning, Deep learning, Hyperparameter Optimization, Infrared region","lastPublishedDoi":"10.21203/rs.3.rs-4812441/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4812441/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOptical filters are specialized structures designed to selectively transmit specific regions of the optical spectrum while blocking others. These filters achieve their desired properties using a variety of materials and methods. This work focuses on designing and optimizing multilayer optical filters utilizing Machine Learning (ML) and Deep Learning (DL) techniques. A dataset is created from Finite Difference Time Domain (FDTD) simulations of Germanium (Ge) substrates coated with alumina (Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e) or silica (SiO\u003csub\u003e2\u003c/sub\u003e). The dataset consists of bands 3\u0026ndash;5, typical for medium-wave infrared (MWIR) and long-wave infrared (LWIR) bands, and includes reflectance values for wavelengths varying between 3 \u0026micro;m and 12 \u0026micro;m. Six ML algorithms and a DL model, including artificial neural networks (ANN) and convolutional neural networks (CNN), are evaluated to determine the most effective approach for predicting reflectance properties. Bayesian optimization is used to fine-tune the hyperparameters of the DL model, achieving optimum performance. The results show that ML models, particularly decision tree, random forest, and bagging methods, outperform the DL model in predicting reflectance values and provide a valuable reference for designing and fabricating optical thin-film filters.\u003c/p\u003e","manuscriptTitle":"Deep Learning and Machine Learning Based Highly Accurate Prediction of Reflection for Multi Layers Anti-Reflection Coatings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 08:46:21","doi":"10.21203/rs.3.rs-4812441/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-10T09:04:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-10T01:59:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54049929140061221976324015409872894321","date":"2024-10-04T06:49:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250622744354106795378621163913747560399","date":"2024-10-04T05:59:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-01T10:29:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214503861153961552778976401495139625063","date":"2024-10-01T03:07:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44424765368272373917132474361367315641","date":"2024-09-30T19:36:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27788771356405106446162534899857298034","date":"2024-09-19T11:34:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185927325190839884053231352238209812371","date":"2024-08-23T08:09:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-21T12:51:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-27T12:11:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-27T10:10:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Optical and Quantum Electronics","date":"2024-07-27T10:00:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"optical-and-quantum-electronics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oqel","sideBox":"Learn more about [Optical and Quantum Electronics](https://www.springer.com/journal/11082)","snPcode":"11082","submissionUrl":"https://submission.nature.com/new-submission/11082/3","title":"Optical and Quantum Electronics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9c87c8c4-e03c-4b69-bc27-bfa700e336d5","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:02:21+00:00","versionOfRecord":{"articleIdentity":"rs-4812441","link":"https://doi.org/10.1007/s11082-024-08006-x","journal":{"identity":"optical-and-quantum-electronics","isVorOnly":false,"title":"Optical and Quantum Electronics"},"publishedOn":"2025-01-11 15:57:38","publishedOnDateReadable":"January 11th, 2025"},"versionCreatedAt":"2024-08-22 08:46:21","video":"","vorDoi":"10.1007/s11082-024-08006-x","vorDoiUrl":"https://doi.org/10.1007/s11082-024-08006-x","workflowStages":[]},"version":"v1","identity":"rs-4812441","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4812441","identity":"rs-4812441","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}