[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125011-en":3,"doc-seo-125011-105":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},125011,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Meent - Differentiable Electromagnetic Simulator for Machine Learning","Electromagnetic simulation underpins the analysis and design of sub-wavelength devices such as solar cells, semiconductor structures, image sensors, future displays, and integrated photonics. Conventional iterative EM solvers are computationally expensive and often limit optimization quality, motivating machine learning adoption. To bridge optics and ML workflows, meent delivers a Python-based RCWA simulator with automatic differentiation. The work demonstrates dataset generation for neural operators, reinforcement learning for nanophotonic optimization, and gradient-based solvers for inverse problems.","arXiv :2406 . 12904v1 [ cs .LG] 11 Jun 2024  \nMeent: Differentiable Electromagnetic Simulator for Machine Learning  \nYongha Kim 1* , Anthony W. Jung 1* , Sanmun Kim2, 3* , Kevin Octavian2 , Doyoung Heo2 , Chaejin Park 1, 2 , Jeongmin Shin2 , Sunghyun Nam2 , Chanhyung Park2 , Juho Park2 , Sangjun Han2 , Jinmyoung Lee 1 , Seolho Kim 1 , Min Seok Jang2†, Chan Y. Park 1†  \n1 KC Machine Learning Lab, Seoul, Republic of Korea  \n2 KAIST, Daejeon, Republic of Korea  \n3 Caltech, Pasadena, California 91125, United States  \nAbstract  \nElectromagnetic (EM) simulation plays a crucial role in analyzing and designing devices with sub-wavelength scale structures such as solar cells, semiconductor devices, image sensors, future displays and integrated photonic devices. Specifically, optics problems such as estimating semiconductor device structures and designing nanophotonic devices provide intriguing research topics with far-reaching real world impact. Traditional algorithms for such tasks require iteratively refining parameters through simulations, which often yield sub-optimal results due to the high computational cost of both the algorithms and EM simulations. Machine learning (ML) emerged as a promising candidate to mitigate these challenges, and optics research community has increasingly adopted ML algorithms to obtain results surpassing classical methods across various tasks. To foster a synergistic collaboration between the optics and ML communities, it is essential to have an EM simulation software that is user-friendly for both research communities. To this end, we present meent, an EM simulation software that employs rigorous coupled-wave analysis (RCWA) . Developed in Python and equipped with automatic differentiation (AD) capabilities, meent serves as a versatile platform for integrating ML into optics research and vice versa. To demonstrate its utility as a research platform, we present three applications of meent: 1) generating a dataset for training neural operator, 2) serving as an environment for the reinforcement learning of nanophotonic device optimization, and 3) providing a solution for inverse problems with gradient-based optimizers. These applications highlight meent’s potential to advance both EM simulation and ML methodologies. The code is available at [https://github.com/kc-ml2/meent](https://github.com/kc-ml2/meent with the MIT license to promote the)[ with the MIT license to promote the](https://github.com/kc-ml2/meent with the MIT license to promote the)[ ](https://github.com/kc-ml2/meent with the MIT license to promote the)[cross-polinations of ideas among academic researchers and industry practitioners.](cross-polinations of ideas among academic researchers and industry practitioners.)  \n1 Introduction  \nHarnessing light-matter interaction to design or analyze a device with sub-wavelength scale structure has a wide range of applications, including high-efficiency solar cells [1, 2], ultra-thin metalenses and displays [3, 4], optical metrology for semiconductor fabrication [5, 6], X-ray diffraction for material analysis [7, 8], optical computation [9, 10], and so on. Their implication to the real world is far-reaching, leading to improved renewable energy production, enhanced user experience, and nextgeneration computation. Electromagnetic (EM) simulation plays a crucial role in such applications,  \n* Equal contribution. E-mail: [yongha@kc-ml2.com](yongha@kc-ml2.com), [anthony@kc-ml2.com](anthony@kc-ml2.com), [skim6@caltech.edu](skim6@caltech.edu)  \n†Corresponding author. E-mail: [jang.minseok@kaist.ac.kr](jang.minseok@kaist.ac.kr), [chan.y.park@kc-ml2.com](chan.y.park@kc-ml2.com)  \nPreprint. Under review.  \nFigure 1: Summary. Simulation Algorithm depicts the process flow of electromagnetic simulation algorithm, namely RCWA, in meent. Applications present the most representative problems that meent can be utilized.  \nwhich also poses a challenging problem due to its time-consuming nature for precise calculation [","cbCailhniJR9VM8m","https://ap.wps.com/l/cbCailhniJR9VM8m","pdf",18351282,1,53,"English","en",105,"# Abstract\n# Introduction\n## Light-matter interaction and applications\n## Challenges in ML-enabled computational optics\n## meent as a Python-native differentiable RCWA simulator\n## Representative research applications","[{\"question\":\"What problem does meent target in machine-learning-based electromagnetic design?\",\"answer\":\"It addresses the high computational cost and iterative nature of traditional EM simulations and their limited compatibility with ML frameworks and automatic differentiation.\"},{\"question\":\"How does meent enable differentiable electromagnetic simulation?\",\"answer\":\"meent is implemented in Python using rigorous coupled-wave analysis (RCWA) and includes automatic differentiation capabilities to support gradient-based learning and optimization.\"},{\"question\":\"What research applications are demonstrated for meent?\",\"answer\":\"The document presents three applications: generating datasets for training neural operators, acting as an environment for reinforcement learning of nanophotonic optimization, and solving inverse problems with gradient-based optimizers.\"}]","Meent - 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