[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121807-en":3,"doc-seo-121807-105":29,"detail-sidebar-cat-0-en-105":89},{"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":11},121807,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A Machine Learning-Based Model for Characterizing Stationary-and-Dynamic Behavior of VCSEL","We propose a machine learning-based framework to acquire the parameters defining both stationary and dynamic behavior of a VCSEL. Circuit-level simulations of light-current (L-I) characteristics and S21 responses train the model, mapping measured curves to the circuit parameters. Training is validated through relative prediction error, with promising results. The approach aims to reduce the time and effort of brute-force parameter fitting for complex physical laser effects.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nA Machine Learning-Based Model for Characterizing Stationary-and-Dynamic Behavior of VCSEL  \nOriginal  \nA Machine Learning-Based Model for Characterizing Stationary-and-Dynamic Behavior of VCSEL / Khan, Ihtesham; Marchisio, Andrea; Tunesi, Lorenzo; Masood, MUHAMMAD UMAR; Ghillino, Enrico; Curri, Vittorio; Carena, Andrea; Bardella, Paolo. -ELETTRONICO. - (2023), pp. 1-2. (Intervento presentato al convegno CLEO: Science and Innovations tenutosi a San Jose, CA, United States nel 7-12 May 2023) [10 . 1364/CLEO_AT.2023.JW2A. 141] .  \nAvailability:  \nThis version is available at: 11583/2980624 since: 2023-08-23T09:23:32Z  \nPublisher:  \nOptica Publ.  \nPublished  \nDOI:10 . 1364/CLEO_AT.2023.JW2A.141  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nOptica Publishing Group (formely OSA) postprint/Author's Accepted Manuscript  \n“© 2023 Optica Publishing Group. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited.”  \n(Article begins on next page)  \n28 March 2025  \nA Machine Learning-Based Model for Characterizing Stationary-and-Dynamic Behavior of VCSEL  \nIhtesham Khan(1), Andrea Marchisio(1), Lorenzo Tunesi(1), Muhammad Umar Masood(1), Enrico Ghillino(2), Vittorio Curri (1) , Andrea Carena(1) , Paolo Bardella(1)  \n(1) Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, Italy  \n(2) Synopsys, Inc., 400 Executive Blvd Ste 101, Ossining, NY 10562, United States paolo.bardella@polito.it  \nAbstract: We propose a machine learning-based framework to acquire parameters that define stationary-and-dynamic behavior of VCSEL. Circuit-level simulations of light-current and S21 are used to train the model. In terms of relative-prediction-error promising results are achieved. © 2022 The Author(s)  \n1. Introduction  \nIn recent years, many physical models have been presented to characterize the complex behavior of edge-emitting or vertical-cavity laser diodes. These tools help designers understand laser behavior and maximize device attributes by accurately describing sophisticated physical effects. As a drawback, the number of involved physical parameters (geometrical properties, material characteristics, electrical and thermal effects) can make it challenging to find a correct set of parameters fitting experimental laser measurements, starting with the fundamental Light-Current (L-I) characteristics and the Small Signal Modulation Responses (S21) . Extracting physical parameters from experimental curves can be time-consuming, requiring brute-force minimization, trial-and-error tactics, or regression analysis. We present a Machine Learning (ML) approach that can extract from experimental measurements the parameters required by a circuit-level model of a Vertical Cavity Surface Emitting Laser (VCSEL) developed in Synopsys OptSim [1] . We propose a single ML-based agent that can cope with all the electrical, optical, and thermal effects considered in OptSim, unlike prior research that concentrated on edge-emitting devices [2] or required two distinct simulations to account for temperature-dependent effects [3] .  \n2. Vertical Cavity Surface Emitting Laser Model and Dataset Generation  \nThe VCSEL model implemented in OptSim derives from the mean-field model proposed in [4] . The temporal evolution the photon number in the cavity S and their phase φ is described as  \nSt = − ~~ ~~Sτ~~ ~~p + β~~ ~~spτNn0 + G~~ ~~[γ00(N0~~1~~NεtS)~~ ~~−γ01N1]S (1) ~~ ~~φt =  G~~ ~~[γ00(N0~~ ~~tεrS)−γ01N1] (2) with τp photon lifetime and βsp spontaneous emission coefficient, G gain, τn carrier lifetime, ε gain saturation factor, α linewidth enhancement factor. The carrier numbers N0 and N1 are the","cbCaiqiA5g7GVKfN","https://ap.wps.com/l/cbCaiqiA5g7GVKfN","pdf",977958,1,3,"English","en",105,"# Introduction\n# Vertical Cavity Surface Emitting Laser Model and Dataset Generation","[{\"question\":\"What does the proposed machine learning framework do for VCSELs?\",\"answer\":\"It learns a mapping from simulated L-I and S21 behavior to the circuit model parameters that define stationary and dynamic VCSEL behavior.\"},{\"question\":\"Which simulation data are used to train the model?\",\"answer\":\"Circuit-level simulations generate light-current (L-I) points and S21 responses under multiple temperatures to form the training dataset.\"},{\"question\":\"How is the model parameter fitting expected to improve over traditional methods?\",\"answer\":\"By avoiding time-consuming brute-force minimization and trial-and-error regression, the ML approach extracts parameters directly from measurements.\"}]","A Machine Learning-Based Model for Characterizing Stationary-and-Dynamic Behavior of VCSEL | PDF",1785806965,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":28},"a-machine-learning-based-model-for-characterizing-stationary-and-dynamic-behavior-of-vcsel","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/a-machine-learning-based-model-for-characterizing-stationary-and-dynamic-behavior-of-vcsel/121807/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What does the proposed machine learning framework do for VCSELs?","Question",{"text":73,"@type":74},"It learns a mapping from simulated L-I and S21 behavior to the circuit model parameters that define stationary and dynamic VCSEL behavior.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which simulation data are used to train the model?",{"text":78,"@type":74},"Circuit-level simulations generate light-current (L-I) points and S21 responses under multiple temperatures to form the training dataset.",{"name":80,"@type":71,"acceptedAnswer":81},"How is the model parameter fitting expected to improve over traditional methods?",{"text":82,"@type":74},"By avoiding time-consuming brute-force minimization and trial-and-error regression, the ML approach extracts parameters directly from measurements.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]