[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126132-en":3,"doc-seo-126132-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":20,"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},126132,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning force field for thermal oxidation of silicon","Silicon and its native oxide SiO2 remain central to semiconductor technology, yet ultra-thin oxide layers now demand deeper insight into the atomic configurations at the Si/SiO2 interface. Classical force fields provide flexibility but limited accuracy, while ab initio approaches are highly reliable yet extremely costly. A machine learning force field (MLFF) is trained to simulate dry thermal oxidation of a Si substrate using density functional theory generated training data. The resulting structures match ab initio simulations and experimental observations, and show major improvements over a classical force field, with the potential released publicly in an open-access repository.","RESEARCH ARTICLE | OCTOBER 10 2024  \nMachine learning force field for thermal oxidation of silicon  \nLukas Cvitkovich 􀀤  ; Franz Fehringer; Christoph Wilhelmer  ; Diego Milardovich  ; Dominic Waldhör  ; Tibor Grasser   \nJ. Chem. Phys. 161, 144706 (2024)  \n[https://doi.org/10.1063/5.0220091](https://doi.org/10.1063/5.0220091)  \n􀀪  \nView Online  \n􀀮  \nExport Citation  \nArticles You May Be Interested In  \nImproving molecular force fields across configurational space by combining supervised and unsupervised machine learning  \nJ. Chem. Phys. (March 2021)  \nNeural network potential from bispectrum components: A case study on crystalline silicon  \nJ. Chem. Phys. (August 2020)  \nOn-the-fly machine learned force fields for the study of warm dense matter: Application to diffusion and viscosity of CH  \nPhys. Plasmas (April 2024)  \n21 December 2024 13:17:13  \nThe Journal  \nof Chemical Physics  \nARTICLE  \n[pubs.aip.org/aip/jcp](pubs.aip.org/aip/jcp)  \nMachine learning force field for thermal oxidation of silicon  \n\n| Cite as: J. Chem. Phys. 161, 144706 (2024); doi: 10. 1063/5.0220091 Submitted: 22 May 2024 • Accepted: 24 September 2024 •\u003Cbr>Published Online: 10 October 2024 |  |  |  |\n| --- | --- | --- | --- |\n| Lukas Cvitkovich,a)  Franz Fehringer, Christoph Wilhelmer,  Diego Milardovich,  Dominic Waldhör,  and Tibor Grasser  |  |  |  |\n| AFFILIATIONS\u003Cbr>Institute for Microelectronics, Technische Universität Wien, 1040 Wien, Austria\u003Cbr>a)Author to whom correspondence should [be addressed:](be addressed: cvitkovich@iue.tuwien.ac.at)[ cvitkovich@iue.tuwien.ac.at](be addressed: cvitkovich@iue.tuwien.ac.at) |  |  |  |\n| ABSTRACT\u003Cbr>Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO2 are still atthe heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in the down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO2 interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab initio methods give much better results but are extremely costly. Machine learning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data are generated by density functional theory calculations. The obtained structures are in line with ab initio simulations and with experimental observations. Compared to a classical force field, the most recent reactive force field, the resulting configurations are vastly improved. Our potential is publicly available in an open-access repository.\u003Cbr>© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)). [https://doi.org/10.1063/5.0220091](https://doi.org/10.1063/5.0220091) |  |  |  |\n\nI. INTRODUCTION  \nSilicon has played a major role in semiconductor device technology for more than half a century and continues to find a broad range of novel applications spanning from single-electron devices1 to spintronics2,3 and semiconductor spin qubits.4  \nOne of the most important reasons for the extensive use of Si is that its native oxide SiO2 allows the production of semiconductor/insulator interfaces of exceptional quality.5 Highly optimized devices such as MOSFETs benefit from low defect densities at the interface and convenient growth of the oxide directly onto a Si substrate by thermal oxidation.6 Although pure SiO2 is being gradually substituted as a gate dielectric by other materials possessing significantly higher dielectric cons","cbCaiqrONd9XwrQe","https://ap.wps.com/l/cbCaiqrONd9XwrQe","pdf",5357598,9,1,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"为什么需要用于硅热氧化的机器学习力场？\",\"answer\":\"经典力场计算灵活但精度有限，且难以准确描述初始氧化阶段；从头算方法精度高但计算成本极高。机器学习力场旨在兼顾两者优势。\"},{\"question\":\"MLFF 的训练数据来自哪里？\",\"answer\":\"训练数据由密度泛函理论（DFT）计算生成。\"},{\"question\":\"与经典力场相比，模型的结果如何？\",\"answer\":\"相较于经典力场以及更早的反应性力场对照，得到的构型显著改进，并与从头算模拟及实验观察保持一致。\"}]","Machine learning force field for thermal oxidation of silicon | PDF",1785903325,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-force-field-for-thermal-oxidation-of-silicon","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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-force-field-for-thermal-oxidation-of-silicon/126132/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"为什么需要用于硅热氧化的机器学习力场？","Question",{"text":76,"@type":77},"经典力场计算灵活但精度有限，且难以准确描述初始氧化阶段；从头算方法精度高但计算成本极高。机器学习力场旨在兼顾两者优势。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"MLFF 的训练数据来自哪里？",{"text":81,"@type":77},"训练数据由密度泛函理论（DFT）计算生成。",{"name":83,"@type":74,"acceptedAnswer":84},"与经典力场相比，模型的结果如何？",{"text":85,"@type":77},"相较于经典力场以及更早的反应性力场对照，得到的构型显著改进，并与从头算模拟及实验观察保持一致。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"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":107,"slug":138},19,"General","general"]