[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121745-en":3,"doc-seo-121745-105":30,"detail-sidebar-cat-0-en-105":90},{"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},121745,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Automated model for characterization of VCSEL circuit-level parameters using machine learning","A machine learning-based model is proposed to extract physical parameters that characterize the stationary and dynamic behavior of a VCSEL. Training relies on circuit-level simulations of light-current (L-I) and S21 characteristics, enabling the model to infer parameters of a circuit-level VCSEL framework. The approach is designed to cope with electrical, optical, and thermal effects handled in Synopsys OptSim, achieving excellent predictive accuracy with low relative error on validation results.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nAutomated model for characterization of VCSEL circuit-level parameters using machine learning  \nOriginal  \nAutomated model for characterization of VCSEL circuit-level parameters using machine learning / Marchisio, Andrea; Khan, Ihtesham; Tunesi, Lorenzo; Masood, Muhammad Umar; Ghillino, Enrico; Curri, Vittorio; Carena, Andrea; Bardella, Paolo. -ELETTRONICO. - (2023), pp. 264-266. (Intervento presentato al convegno European Conference on Integrated Optics tenutosi a Enschede, Paesi Bassi nel 19-21 Aprile 2023) .  \nAvailability:  \nThis version is available at: 11583/2978489 since: 2023-05-14T17:07:21Z  \nPublisher:  \nEuropean Conference on Integrated Optics  \nPublished DOI:  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n17 October 2023  \nAutomated Model for Characterization of VCSEL Circuit-level Parameters Using Machine Learning  \nAndrea Marchisio1, Ihtesham Khan1, Lorenzo Tunesi1, Muhammad Umar Masood1,  \nEnrico Ghillino2, Vittorio Curri1, Andrea Carena1, and Paolo Bardella1  \n1 Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, Italy  \n2 Synopsys, Inc., 400 Executive Blvd Ste 101, Ossining, NY 10562, United States  \npaolo.bardella@polito.it  \nWe propose a machine learning-based model to extract physical parameters characterizing stationary-and-dynamic behavior of a VCSEL. The model is trained with circuit-level simulations of light-current and S21 characteristics. Excellent results are achieved as a relative  \nprediction error.  \nKeywords: machine learning, VCSEL, physical model, parameters extraction  \nINTRODUCTION  \nIn the last decades, a significant number of physical models have been proposed in the literature to describe the complex behavior of edge-emitting or vertical-cavity laser diodes, either with a phenomenological or an empirical approach. With an accurate description of advanced physical effects, these tools allow a better understanding of the laser behavior and the optimization of device properties according to designers’ needs. As a drawback, the number of involved physical parameters (geometrical properties, material characteristics, electrical and thermal effects) is generally so large that it may be challenging to find a correct set of parameters fitting experimental laser measurements, starting from the fundamental light-current (L-I) characteristics and the small signal modulation responses (S21) . The extraction of these physical parameters from experimental curves can be time-consuming, since it often relies on brute-force minimization routines, trial-and-error approaches, or regression analysis. We propose a Machine Learning (ML) approach to the problem, based on Deep Learning (DP), which can extract from experimental measurements the parameters required by a circuit-level model of a Vertical-Cavity Surface-Emitting Laser (VCSEL) [1], implemented in Synopsys OptSim™ [2] . With respect to other works, which focused on edge emitting devices [3] or required two separate simulations to take into account temperature-dependent effects [4], we propose a single DL-based agent that can deal with all the electrical, optical, and thermal effects considered in OptSim™ .  \nVERTICAL-CAVITY SURFACE-EMITTING LASER MODEL  \nFor the description of the optical and electrical properties of the VCSEL, we rely on the model available in Synopsys OptSim™ [2], which implements and expands the circuit-level model originally proposed in [1] . There, the explicit spatial dependence of the number of carriers N(r; t ) in the transverse plane is eliminated by assuming cylindrical symmetry and by introducing a two-term Bessel series expansion:  \nN (r; t ) = N0 (t )􀀀 N1 (t )J0 (s1r=R)  \nwith J0 and J1 Bessel functions of the first kind, s1 first nonzero root of J1, and R effective radius of the active layer. Th","cbCaifqbWVVAF5k3","https://ap.wps.com/l/cbCaifqbWVVAF5k3","pdf",320389,1,4,"English","en",105,"# Introduction\n# Vertical-Cavity Surface-Emitting Laser Model\n# Dataset Generation & Machine Learning Engine","[{\"question\":\"What problem does the proposed approach address for VCSEL modeling?\",\"answer\":\"It targets the difficulty of fitting many physical parameters to experimental VCSEL curves, which is often time-consuming when using brute-force minimization or trial-and-error regression.\"},{\"question\":\"Which data are used to train the machine learning model?\",\"answer\":\"The model is trained with circuit-level simulations of light-current (L-I) and S21 characteristics.\"},{\"question\":\"How does the method relate to the Synopsys OptSim circuit-level VCSEL model?\",\"answer\":\"The work uses the OptSim circuit-level model as the basis and trains a single deep-learning agent that covers electrical, optical, and thermal effects accounted for in OptSim.\"}]","Automated 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problem does the proposed approach address for VCSEL modeling?","Question",{"text":74,"@type":75},"It targets the difficulty of fitting many physical parameters to experimental VCSEL curves, which is often time-consuming when using brute-force minimization or trial-and-error regression.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which data are used to train the machine learning model?",{"text":79,"@type":75},"The model is trained with circuit-level simulations of light-current (L-I) and S21 characteristics.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the method relate to the Synopsys OptSim circuit-level VCSEL model?",{"text":83,"@type":75},"The work uses the OptSim circuit-level model as the basis and trains a single deep-learning agent that covers electrical, optical, and thermal effects accounted for in 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