[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121808-en":3,"doc-seo-121808-105":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},121808,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine Learning Aided Control of Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulators","A digital model of a dual-polarization IQ ultra-wideband indium phosphide Mach-Zehnder modulator is generated using machine learning methods. The learned model serves as a testbed to run optimization algorithms that automatically set modulator control voltages under different operative conditions. The strategy searches for the optimal bias point by leveraging predicted device behavior, and is validated in practice with a structure-agnostic, measurement-limited, minimum-equipment laboratory workflow.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine Learning Aided Control of Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulators  \nOriginal  \nMachine Learning Aided Control of Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulators / D'Ingillo, Rocco; D'Amico, Andrea; Usmani, Fehmida; Borraccini, Giacomo; Straullu, Stefano; Siano, Rocco; Belmonte, Michele; Curri, Vittorio. -ELETTRONICO. - (2023), pp. 1-3. (Intervento presentato al convegno 2023 International Conference on Photonics in Switching and Computing (PSC) tenutosi a Mantova, Italy nel 26-29 September 2023)[10 . 1109/PSC57974 .2023. 10297214] .  \nAvailability:  \nThis version is available at: 11583/2983886 since: 2023-11-20T14:49:55Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/PSC57974.2023.10297214  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n11 February 2024  \nMachine Learning Aided Control of an Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulator  \nRocco D’Ingillo(1) , Andrea D’Amico(1) , Fehmida Usmani(1),(2) , Giacomo Borraccini(1) , Stefano Straullu(3) ,  \nRocco Siano(4) , Michele Belmonte(4) , Vittorio Curri(1)  \n(1) Politecnico di Torino, Italy, rocco.dingillo@polito. it ;  \n(2) National University of Sciences & Technology (NUST), Pakistan; (3) LINKS Foundation, Italy; (4) Lumentum, Italy  \nAbstract A digital model of a dual-polarization IQ ultra-wideband indium phosphide Mach-Zehnder  \nmodulator is obtained through machine learning techniques. The model is used to test optimization algorithms that automatically set the modulator control voltages under different operative conditions finding the optimum bias point. ©2023 The Author(s)  \nIntroduction  \nMach-Zehnder Modulators (MZMs) play a critical role in optical communication, and their performance heavily relies on device bias point control. Indium Phosphide (InP) technology is promising for MZM fabrication as it can be easily integrated with other photonics components, enabling a more consistent usage of photonic integrated circuits (PICs)[1] . On the other hand, the material characteristics make MZM control more complex due to the nonlinear behavior of the device[2], making the search for the optimal bias point challenging. There are two main techniques to optimize the operating bias point control: optical power-based[3]–[5] and dither-based techniques[6]–[8] . Power-based techniques use photodetectors (PDs) to monitor the modulator’s output optical power, adjusting the bias voltage until the desired optical power is achieved. Conversely, dither-based techniques use a sinusoidal voltage signal to modulate the bias voltage and monitor the modulator output to maintain a constant output by adjusting the bias voltage[9] . The power-based technique is simpler and can be implemented by means of a minimum laboratory equipment, while the dither-based technique is more robust to noise and optical power fluctuations at the price of more complex circuitry. This paper presents a novel approach to search and control the bias point of an InP ultra-wideband (UWB) dual polarization (DP) IQ-MZM using machine learning (ML) techniques to generate a digital model of the MZM. In recent years, the use of ML and artificial intelligence (AI) has spread widely in the photonics industry[10],[11] . In this study, an AI-based system learns the behavior of the MZM and applies optimization algorithms on the predicted model to find its optimal bias point autom","cbCaijE5AUGxlGSO","https://ap.wps.com/l/cbCaijE5AUGxlGSO","pdf",2471141,1,5,"English","en",105,"# Introduction\n# Mach-Zehnder Modulator Bias Point Control","[{\"question\":\"What is the main goal of the proposed approach?\",\"answer\":\"To automatically search and control the optimal bias point of an ultra-wideband indium phosphide dual-polarization IQ Mach-Zehnder modulator using machine learning techniques.\"},{\"question\":\"How is the modulator controlled in this work?\",\"answer\":\"The method learns a digital model of the modulator and applies optimization algorithms to set control voltages under varying operating conditions to reach the best bias point.\"},{\"question\":\"How was the approach validated in the laboratory?\",\"answer\":\"By applying the ML-aided power-based bias point control algorithm to the actual component, using a structure-agnostic setup with different modulator settings while relying on a limited number of measurements and minimal laboratory equipment.\"}]","Machine Learning Aided Control of Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulators | 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