[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121744-en":3,"doc-seo-121744-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121744,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Two-step machine learning assisted extraction of VCSEL parameters - research report","A machine learning assisted procedure is proposed to extract Vertical Cavity Surface Emitting Lasers (VCSELs) parameters from Light-Current (L-I) and S21 curves using a two-step approach for high prediction accuracy. In the first step, temperature-independent parameters are inferred via a deep neural network trained on 10,000 mean-field VCSEL simulations, then retrieved from experimental curves at a fixed temperature. In the second step, temperature-dependent parameters are learned using a second 10,000-simulation dataset while keeping the extracted parameters constant and varying operation temperature.","POLITECNICO DI TORINO Repository ISTITUZIONALE  \nTwo-step machine learning assisted extraction of VCSEL parameters  \nOriginal  \nTwo-step machine learning assisted extraction of VCSEL parameters / Khan, Ihtesham; Masood, Muhammad Umar; Tunesi, Lorenzo; Ghillino, Enrico; Carena, Andrea; Curri, Vittorio; Bardella, Paolo. -ELETTRONICO. - (2023), p. 49.(Intervento presentato al convegno SPIE Opto tenutosi a San Francisco, California, United States nel 28 January-3 February 2023) [10 . 1117/12 .2650220] .  \nAvailability:  \nThis version is available at: 11583/2977541 since: 2023-03-28T15:49:24Z  \nPublisher: SPIE  \nPublished  \nDOI:10.1117/12.2650220  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nSPIE postprint/Author's Accepted Manuscript e/o postprint versione editoriale/Version of Record con  \nCopyright 2023 Society of PhotoOptical Instrumentation Engineers (SPIE) . One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.  \n(Article begins on next page)  \n17 October 2023  \nPROCEEDINGS OF SPIE  \n[SPIEDigitalLibrary.org/conference-proceedings-of-spie](SPIEDigitalLibrary.org/conference-proceedings-of-spie)  \nTwo-step machine learning assisted extraction of VCSEL parameters  \nIhtesham Khan, Muhammad Umar Masood, Lorenzo Tunesi, Enrico Ghillino, Andrea Carena, et al.  \nIhtesham Khan, Muhammad Umar Masood, Lorenzo Tunesi, Enrico Ghillino, Andrea Carena, Vittorio Curri, Paolo Bardella, \"Two-step machine learning assisted extraction of VCSEL parameters,\" Proc. SPIE 12415, Physics and Simulation of Optoelectronic Devices XXXI, 124150P (10 March 2023); doi: 10.1117/12.2650220  \nEvent: SPIE OPTO, 2023, San Francisco, California, United States  \nDownloaded From: [https://www.spiedigitallibrary.org/conference-proceedings-of-spie on](https://www.spiedigitallibrary.org/conference-proceedings-of-spie on) 28 Mar 2023 Terms of Use: [https://www.spiedigitallibrary.org/terms-of-use](https://www.spiedigitallibrary.org/terms-of-use)  \nTwo-step Machine Learning Assisted Extraction of VCSEL parameters  \nIhtesham Khana , Muhammad Umar Masooda , Lorenzo Tunesia , Enrico Ghillinob , Andrea Carenaa , Vittorio Curria , and Paolo Bardellaa  \na Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy  \nb Synopsys, Inc. , Ossining, NY 10562, United States  \nABSTRACT  \nWe propose a Machine Learning (ML) assisted procedure to extract Vertical Cavity Surface Emitting Lasers (VCSELs) parameters from Light-Current (L-I) and S21 curves using a two-step algorithm to ensure high accuracy of the prediction. In the first step, temperature effects are not included and a Deep Neural Network (DNN) is trained on a dataset of 10000 mean-field VCSEL simulations, obtained changing nine temperature-independent parameters. The agent is used to retrieve those parameters from experimental results at a fixed temperature. Secondly, additional nine temperature-dependent parameters are analyzed while keeping as constant the extracted ones and changing the operation temperature. In this way a second dataset of 10000 simulations is created anda new agent in trained to extract those parameters from temperature-dependent L-I and S21 curves.  \nKeywords: Vertical Cavity Surface Emitting Lasers, Machine Learning, Deep Learning,Parameters Extraction.  \n1. INTRODUCTION  \nVCSEL have been accurately described by a number of computationally effective models that have been developed in recent years, many of them being also available in commercial software aimed at simulating VCSEL sources as a single component or as part of larger optoelectronic systems. The complexity of these models, generally including a description of carrier and photon dyn","cbCaikScWzgf2kgR","https://ap.wps.com/l/cbCaikScWzgf2kgR","pdf",385244,1,6,"English","en",105,"# Abstract\n## Proposed two-step ML procedure\n## Step 1: Temperature-independent parameter extraction\n## Step 2: Temperature-dependent parameter extraction\n## VCSEL model and simulation framework","[{\"question\":\"What inputs are used to extract VCSEL parameters?\",\"answer\":\"The method extracts parameters from Light-Current (L-I) and S21 curves.\"},{\"question\":\"How does the two-step algorithm improve accuracy?\",\"answer\":\"It separates temperature-independent effects from temperature-dependent effects, using two dedicated neural-network training stages to refine predictions.\"},{\"question\":\"What data is used to train the deep neural network(s)?\",\"answer\":\"Each step is trained on 10,000 mean-field VCSEL simulations, with temperature handling tailored to the specific stage.\"}]","Two-step machine learning assisted extraction of VCSEL parameters - research report | PDF",1785806612,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"two-step-machine-learning-assisted-extraction-of-vcsel-parameters-research-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/two-step-machine-learning-assisted-extraction-of-vcsel-parameters-research-report/121744/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What inputs are used to extract VCSEL parameters?","Question",{"text":75,"@type":76},"The method extracts parameters from Light-Current (L-I) and S21 curves.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the two-step algorithm improve accuracy?",{"text":80,"@type":76},"It separates temperature-independent effects from temperature-dependent effects, using two dedicated neural-network training stages to refine predictions.",{"name":82,"@type":73,"acceptedAnswer":83},"What data is used to train the deep neural network(s)?",{"text":84,"@type":76},"Each step is trained on 10,000 mean-field VCSEL simulations, with temperature handling tailored to the specific stage.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]