[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119976-en":3,"doc-seo-119976-105":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},119976,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine learning for advancing low-temperature plasma modeling and simulation - Review","Machine learning is increasingly reshaping low-temperature plasma modeling and simulation, offering substantial benefits for data-driven approaches in plasma physics and engineering. A structured survey reviews state-of-the-art methods by covering plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, with representative literature examples. The work also outlines potential advances for plasma science and technology, emphasizing adaptations from other disciplines. The authors argue data-driven methods can reveal both known and unknown unknowns by exposing hidden patterns.","arXiv :2307 .00131v2 [physics .plasm-ph] 14 Dec 2023  \nReview: Machine learning for advancing low-temperature plasma modeling and simulation  \nJan Trieschmanna,b,* , Luca Vialettoa,c, Tobias Gergsa,d  \naTheoretical Electrical Engineering, Department of Electrical and Information Engineering, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany  \nb Kiel Nano, Surface and Interface Science KiNSIS, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany  \ncDepartment of Aeronautics and Astronautics, Stanford University, 496 Lomita Mall, Stanford, CA 94305, United States of America  \nd Chair of Applied Electrodynamics and Plasma Technology, Department of Electrical Engineering and Information Science, Ruhr University Bochum, 44780 Bochum, Germany  \nAbstract. Machine learning has had an enormous impact in many scientific disciplines. Also in the field of lowtemperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and data-driven modeling. In this survey, we approach two main objectives: (a) We review the state-of-the-art, focusing on approaches to low-temperature plasma modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns, but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data.  \nKeywords: artificial intelligence, simulations, neural networks, plasmas.  \n*Jan Trieschmann,  [jt@tf.uni-kiel.de](jt@tf.uni-kiel.de)  \n1 Introduction  \nLow-temperature plasmas (LTPs) consist of a mixture of neutral and charged species in thermal non-equilibrium. The governing discharge physics and chemistry lead to a zoo of (excited) species interacting with bounding surfaces. Their modification (e.g., activation, etching, functionalization) is exploited in the frame of plasma processing (e.g., plasma-enhanced etching, deposition, catalysis) . LTPs also render a dominant part of the many steps required in semiconductor device manufacturing. This may involve a diverse portfolio of plasma discharges, chemical precursors, surface materials, and process recipe steps to achieve desired deposit/etch patterns.1, 2 It may as well include indirect plasma discharges for example in the generation of extreme ultraviolet light  \nfor state of the art 13.5 nm wavelength lithography, required for device feature scales of the order of 10 nm and beyond (for instance termed 3 nm nodes) .3 Modeling and numerical simulation contribute an integral component in the design and optimization of related plasma processes. In the recent past decade, data-driven methods such as machine learning (ML), deep learning (DL), and artificial neural networks (ANNs), among other computational statistics methods, have (re-)gained enormous interest – which may be subsumed under the umbrella term artificial intelligence (AI) . Although these fields have seen a renaissance and have continued to rapidly develop in the past decade, many fundamental concepts have been well established in computational statistics and related disciplines. The past and present interest in the context of plasma processing maybe attributed to the potential for hidden pattern detection in correlated data and, particularly, the efficiency in related optimization tasks.4–12  \nAlthough we limit our scope to modeling and simulation in the following, it should be stressed that ML methods and ANNs have been widely e","cbCaikJNnrmOWiV3","https://ap.wps.com/l/cbCaikJNnrmOWiV3","pdf",3279310,1,77,"English","en",105,"# Introduction\n## Low-temperature plasmas and plasma processing context\n## Modeling, simulation, and the role of AI/ML\n## From experimental diagnostics to data-driven modeling\n# State of the art and review scope\n## Data-driven LTP modeling approaches\n## Connections to plasma fusion and related research areas","[{\"question\":\"What is the main goal of the survey on low-temperature plasma modeling and simulation?\",\"answer\":\"The survey aims to review state-of-the-art approaches for low-temperature plasma modeling and simulation and to provide a perspective on future advances enabled by data-driven methods.\"},{\"question\":\"How does the review organize the topic?\",\"answer\":\"It divides coverage into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, discussing relevant examples from the literature.\"},{\"question\":\"In what ways does the document suggest machine learning can drive future plasma research?\",\"answer\":\"It argues that data-driven methods may uncover hidden patterns in data and therefore help discover both known unknowns and unknown unknowns, enabling new opportunities for plasma science and technology.\"}]","Machine learning for advancing low-temperature plasma modeling and simulation - Review | PDF",1785727393,194,{"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},"machine-learning-for-advancing-low-temperature-plasma-modeling-and-simulation-review","",{"@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/machine-learning-for-advancing-low-temperature-plasma-modeling-and-simulation-review/119976/",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","2026-08-03",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 is the main goal of the survey on low-temperature plasma modeling and simulation?","Question",{"text":75,"@type":76},"The survey aims to review state-of-the-art approaches for low-temperature plasma modeling and simulation and to provide a perspective on future advances enabled by data-driven methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the review organize the topic?",{"text":80,"@type":76},"It divides coverage into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, discussing relevant examples from the literature.",{"name":82,"@type":73,"acceptedAnswer":83},"In what ways does the document suggest machine learning can drive future plasma research?",{"text":84,"@type":76},"It argues that data-driven methods may uncover hidden patterns in data and therefore help discover both known unknowns and unknown unknowns, enabling new opportunities for plasma science and technology.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]