[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123255-en":3,"doc-seo-123255-105":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},123255,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","EllipBench - A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling","Ellipsometry provides an indirect way to measure thin-film optical properties and thickness, but solving the inverse mapping is slow because data fitting typically requires expert intervention. EllipBench addresses this bottleneck with a deep learning approach, delivering a large-scale benchmark dataset covering 98 thin-film material types and 4 substrate categories, totaling over 8 million data points. The work proposes a residual connection and self-attention framework and introduces a reconstruction loss to mitigate multiple-solution ambiguity. Experiments show state-of-the-art performance versus traditional machine learning methods, with dataset and code planned for release upon acceptance.","EllipBench: A Large-scale Benchmark for Machine-learning based Ellipsometry  \nModeling  \nYiming Ma * 1 Xinjie Li * 2 Xin Sun 3 Zhiyong Wang 3 Lionel Z. WANG 1  \narXiv :2407 . 17869v 1 [ cs .LG] 25 Jul 2024  \nAbstract  \nEllipsometry is used to indirectly measure the optical properties and thickness of thin films. However, solving the inverse problem of ellipsometry is time-consuming since it involves human expertise to apply the data fitting techniques. Many studies use traditional machine learning-based methods to model the complex mathematical fitting process. In our work, we approach this problem from a deep learning perspective. First, we introduce a large-scale benchmark dataset to facilitate deep learning methods. The proposed dataset encompasses 98 types of thin film materials and  \n4 types of substrate materials, including metals, alloys, compounds, and polymers, among others. Additionally, we propose a deep learning framework that leverages residual connections and selfattention mechanisms to learn the massive data points. We also introduce a reconstruction loss to address the common challenge of multiple solutions in thin film thickness prediction. Compared to traditional machine learning methods, our framework achieves state-of-the-art (SOTA) performance on our proposed dataset. The dataset and code will be available upon acceptance.  \n1. Introduction  \nThin films play a pivotal role in modern technology and are used in a wide range of industries, including optoelectronic devices, microelectronics, energy, and aerospace. The optical properties of thin films, especially the refractive index and extinction coefficient, are crucial for the design of highperformance devices. Ellipsometry is a traditional method to measure the optical properties and thickness of thin films (Fujiwara, 2007) . The principle of ellipsometry for measuring the optical properties and thickness of thin films is shown in Figure 1. The advantages of ellipsometry are the  \n*Equal contribution 1The Hong Kong Polytechnic University 2Pennsylvania State University 3Tianjin University. Correspondence to: Lionel Z. WANG \u003C[zhe-leo.wang@connect.polyu.hk](zhe-leo.wang@connect.polyu.hk)>.  \nFigure 1 . Schematic of light refraction in thin films and substrates. Light is incident onto a thin film with unknown optical constantsand thickness (n2 , k2 , d) on a substrate with known optical constants (n3 , k3 ) . Ellipsometry measures the parameters Ψ and ∆, with the forward mapping from (n2 , k2 , d) to (Ψ, ∆) . However, inverse mapping does not have an exact analytical formula and relies on data-fitting techniques.  \nability to non-destructively measure the thickness and optical constants of thin films without the need for special sample preparation or damage to the sample, the simplicity of the method compared to other precision methods such as weighing and quantitative chemical analyses, and the ability of ellipsometry to measure very thin films (1 nm) with a high degree of accuracy, 1-2 orders of magnitude better than that of interferometric methods (Tompkins & Irene, 2005) .  \nEllipsometry-based measurements have limitations as they are indirect methods. We cannot directly calculate the optical constants and thickness of a thin film from the measured values of ∆ and Ψ . Instead, it requires a complex process of data analysis and fitting (Collins et al., 2000) . This involves iteratively adjusting the input values for the film’s optical constants and thickness to find the ellipsometric parameters that best match the experimental results. This process is highly time-consuming and labor-intensive, demanding considerable expertise from the operator.  \nThe inverse ellipsometry problem is usually difficult to obtain an analytical solution, and the method of determining the optical constants and thicknesses of thin films by the fitting technique described above relies on the intervention of a human expert to provide an initial guess of the properties of the sample,","cbCaisueUqOrRWvM","https://ap.wps.com/l/cbCaisueUqOrRWvM","pdf",4327638,1,10,"English","en",105,"# Introduction\n## Background and motivation\n## Machine learning for inverse ellipsometry\n## Large-scale benchmark dataset\n## Proposed deep learning framework","[{\"question\":\"What is EllipBench and what problem does it target?\",\"answer\":\"EllipBench is a large-scale benchmark for machine-learning-based ellipsometry modeling. It targets the slow, expert-dependent process of solving the inverse ellipsometry problem through data fitting.\"},{\"question\":\"What does the benchmark dataset include?\",\"answer\":\"The dataset covers 98 types of thin film materials and 4 types of substrate materials. It contains over 8 million data points and is designed for testing machine learning methods.\"},{\"question\":\"How does the proposed deep learning framework improve reconstruction of thin-film thickness?\",\"answer\":\"The framework uses residual connections and self-attention to learn from massive data points efficiently. It also introduces a reconstruction loss to address the one-to-many/multiple-solution challenge in thickness prediction.\"}]","EllipBench - A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling | PDF",1785815506,25,{"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},"ellipbench-a-large-scale-benchmark-for-machine-learning-based-ellipsometry-modeling","",{"@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/ellipbench-a-large-scale-benchmark-for-machine-learning-based-ellipsometry-modeling/123255/",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","2026-08-04",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 EllipBench and what problem does it target?","Question",{"text":75,"@type":76},"EllipBench is a large-scale benchmark for machine-learning-based ellipsometry modeling. It targets the slow, expert-dependent process of solving the inverse ellipsometry problem through data fitting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the benchmark dataset include?",{"text":80,"@type":76},"The dataset covers 98 types of thin film materials and 4 types of substrate materials. It contains over 8 million data points and is designed for testing machine learning methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed deep learning framework improve reconstruction of thin-film thickness?",{"text":84,"@type":76},"The framework uses residual connections and self-attention to learn from massive data points efficiently. It also introduces a reconstruction loss to address the one-to-many/multiple-solution challenge in thickness prediction.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]