{"id":3733,"date":"2026-03-09T17:36:45","date_gmt":"2026-03-09T21:36:45","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/calibre\/?p=3733"},"modified":"2026-03-26T16:24:36","modified_gmt":"2026-03-26T20:24:36","slug":"siemens-eda-2026-at-spie-advanced-lithography","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/calibre\/2026\/03\/09\/siemens-eda-2026-at-spie-advanced-lithography\/","title":{"rendered":"ICYMI: Siemens EDA at 2026 SPIE Advanced Lithography + Patterning"},"content":{"rendered":"\n<figure class=\"wp-block-image alignright size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"250\" height=\"143\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig2-250x143-celebrating-50-years-of-AL.jpg\" alt=\"Celebrating 50 years of SPIE. Image Courtesy of SPIE\n\" class=\"wp-image-3735\"\/><\/figure>\n\n\n\n<p>The 50<sup>th<\/sup> SPIE Advanced Lithography + Patterning symposiums were held February 22-26 this year with the usual enthusiastic and sizable attendance. As always, the Siemens EDA <strong><a href=\"https:\/\/eda.sw.siemens.com\/en-US\/ic\/calibre-manufacturing\" target=\"_blank\" rel=\"noreferrer noopener\">Calibre Manufacturing<\/a><\/strong> group was well represented with two invited papers, a keynote presentation and 13 session papers covering topics on advancing next-generation semiconductor lithography. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Siemens keynote presentation at SPIE Advanced Lithography + Patterning<\/h2>\n\n\n\n<p>On February 24, we were treated to an eye-opening keynote by Dr. James Shiely, Calibre Mask Synthesis R&amp;D Director at Siemens EDA. His presentation, titled \u201c<strong><a href=\"https:\/\/spie.org\/advanced-lithography\/presentation\/Patterning-the-singularity--Computational-lithographys-history-and-future-within\/13980-48\" target=\"_blank\" rel=\"noreferrer noopener\">Patterning the singularity: Computational lithography\u2019s history and future within the accelerating software\/hardware feedback loop<\/a><\/strong>,\u201d took everyone on a tour through computational lithography&#8217;s evolution. Dr. Shiely explored its pivotal role in the technological singularity, where AI could potentially surpass human capabilities. He highlighted the intricate AI-lithography-chip feedback loop and the challenges that temper exponential forecasts. Dr. Shiely&#8217;s vision for sub-atomic scaling, coupled with an open invitation for collaboration, truly set the stage for groundbreaking advancements in AI.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"640\" height=\"396\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/fig1-James-shiely-keynote-spie2026.jpg\" alt=\"Dr. James Shiely, Calibre Mask Synthesis R&amp;D Director at Siemens EDA., gave a keynote talk at SPIE 2026 Advanced Lithography + Patterning. Image courtesy of SPIE. Photographer: Ronald Goossens https:\/\/www.vibrantlightgallery.com\/Events\/Conferences\/SPIE-ALP-2026.\" class=\"wp-image-3736\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/fig1-James-shiely-keynote-spie2026.jpg 640w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/fig1-James-shiely-keynote-spie2026-600x371.jpg 600w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><figcaption class=\"wp-element-caption\">Dr. James Shiely of Siemens EDA., gave a keynote talk at SPIE 2026 Advanced Lithography + Patterning. Image courtesy of SPIE. Photographer: Ronald Goossens https:\/\/www.vibrantlightgallery.com\/<\/figcaption><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\">Siemens invited papers at SPIE Advanced Lithography + Patterning<\/h2>\n\n\n\n<p><strong>Our first invited paper,<\/strong> \u201c<strong><a href=\"https:\/\/spie.org\/advanced-lithography\/presentation\/Inverse-lithography-in-polar-coordinates\/13980-19\" target=\"_blank\" rel=\"noreferrer noopener\">Inverse lithography in polar coordinates<\/a><\/strong>,\u201d was presented by Yuri Granik on February 25. This paper introduced a novel polar coordinate approach to ILT, promising superior accuracy, faster convergence and reduced data volume by optimizing mask representation. This exploration into novel approaches to inverse lithography is a critical technique for optimizing mask patterns to achieve desired wafer features. By utilizing polar coordinates, the research aims to enhance the precision and efficiency of OPC, especially for complex curvilinear designs.<\/p>\n\n\n\n<p><strong>Our second invited paper<\/strong>, \u201c<strong><a href=\"https:\/\/spie.org\/advanced-lithography\/presentation\/Vector-based-site-anchor-decoupling-for-curvilinear-optical-proximity-correction\/13980-32\" target=\"_blank\" rel=\"noreferrer noopener\">Vector-based site-anchor decoupling for curvilinear optical proximity correction,<\/a><\/strong><a href=\"https:\/\/spie.org\/advanced-lithography\/presentation\/Vector-based-site-anchor-decoupling-for-curvilinear-optical-proximity-correction\/13980-32\" target=\"_blank\" rel=\"noopener\">\u201d<\/a> was presented by Sagar Saxena on February 26. \u00a0This paper introduced a new framework for curvilinear OPC, enabling independent site placement and dynamic optimization for improved EPE accuracy and faster convergence. This session delved into advanced methods for curvilinear OPC, focusing on decoupling site-anchor interactions to achieve more accurate and robust pattern correction. The vector-based approach promises improved fidelity and manufacturability for leading-edge technology nodes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Siemens papers at SPIE Advanced Lithography + Patterning<\/h2>\n\n\n\n<p>The 13 other papers and posters delved into a variety of topics, including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Advanced OPC modeling and impact on OPC for dry resist by using low-n mask<\/strong>\u00a0(Joint paper of IMEC, Lam Research, and Siemens EDA). Presented by Dr. Dongbo Xu, this session delved into the critical accuracy of the Calibre OPC model for dry resist processes. Dr Xu shared how our team investigated varying underlayer and PEB conditions, utilizing a BF low-n attenuated phase shift mask and imec pitch 28 nm metal design use-case. The findings, based on wafer exposures on the imec NXE 3400 scanner and HITACHI CD-SEM images, offered crucial insights into the impact of these variations on model accuracy and final OPC performance.<\/li>\n\n\n\n<li><strong>The curvy road to silicon photonics manufacturing<\/strong>\u00a0(Joint paper of CEA-LETI and Siemens EDA). Presented by Sagar Saxena, this paper explored the challenges of manufacturing curvilinear silicon photonic devices and the need for new curvilinear OPC solutions to overcome limitations of traditional Manhattan-based OPC.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"640\" height=\"480\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig3-sagar-saxena-spie2026.jpg\" alt=\"Sagar Saxena of Siemens EDA stands at a lectern while his presentation appears on a screen nearby. \" class=\"wp-image-3737\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig3-sagar-saxena-spie2026.jpg 640w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig3-sagar-saxena-spie2026-600x450.jpg 600w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><figcaption class=\"wp-element-caption\">Sagar Saxena of Siemens EDA presenting at SPIE<\/figcaption><\/figure><\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Guided dataset enrichment for robust process modeling: From random exploration to intelligent feature-aware pattern generation<\/strong>. Presented by Waleed Osama, this paper discussed a feature-aware pattern generation strategy to enrich datasets, improving model fidelity and robustness for advanced semiconductor nodes.<\/li>\n\n\n\n<li><strong>SEM image quality assessment: From wavelet transform to deep learning methods<\/strong>. Presented by Helen Ryding, this paper discussed two No-Reference Image Quality Assessment methods for evaluating wafer SEM image quality, crucial for accurate downstream results.<\/li>\n\n\n\n<li><strong>Large vision model for layout guided SEM image contour extraction<\/strong>. Presented by Sanghyun Choi, this paper introduced a Large Vision Model (LVM) based approach for SEM contour extraction via image segmentation, demonstrating strong zero-shot performance.<\/li>\n\n\n\n<li><strong>Monotonic machine learning (MML) for curvilinear OPC retargeting in NA0.33 EUV single-patterning 28nm metal pitch logic technology<\/strong>\u00a0(Joint paper of IMEC and Siemens EDA). Presented by Hongming Zhang, this paper introduced a cluster-based Monotonic Machine Learning framework for curvilinear patterns, addressing non-linear lithography-to-etch biases in advanced nodes.<\/li>\n\n\n\n<li><strong>Calibration and verification metrics for EUVL stochastic models: Beyond line edge roughness and local CD non-uniformity<\/strong>. Presented by Azat Latypov, PhD, this paper discusses the metrics used to calibrate and verify EUVL stochastic models.<\/li>\n\n\n\n<li><strong>Toward accurate optical model calibration with a near-perfect resist model<\/strong>\u00a0(Joint paper of SAMSUNG and Siemens EDA). This paper unveiled a significant leap forward in achieving highly accurate optical model calibration, enhancing precision and reliability for advanced node manufacturing.<\/li>\n\n\n\n<li><strong>GPU accelerated Bezier mask rule check for high volume production<\/strong>. Presented by Moatsm Eldeeb, this paper examines the benefits of using GPUs to accelerate mask rule check time.<\/li>\n\n\n\n<li><strong>Random 2D metal single patterning capability using 0.55NA EUV lithography<\/strong>\u00a0(Joint paper of Imec and Siemens EDA). Presented by the Imec researcher.<\/li>\n\n\n\n<li><strong>Beyond Manhattan: OPC process window improvement using curvilinear ILT<\/strong>\u00a0(Joint paper of Nanya and Siemens EDA). Presented by the Nanya researcher.<\/li>\n\n\n\n<li><strong>Building systematic defect library with GenAI<\/strong>\u00a0(Joint paper of AMD and Siemens EDA). Presented by the AMD researcher. This paper showcased the power of Generative AI in semiconductor manufacturing, introducing a novel approach to building systematic defect libraries to accelerate defect identification and mitigation.<\/li>\n\n\n\n<li><strong>IGNITE: Sparking optimal OPC strategies through ML-guided site selection<\/strong>. Presented by Steven Lubin. This paper presents a practical approach to intelligent guided navigation for iterative throughput enhancement.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"505\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig5-IGNITE-spie2026-lubin-1024x505.jpeg\" alt=\"Steven Lubin stands at a lectern while his presentation appears on a screen nearby.\" class=\"wp-image-3743\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig5-IGNITE-spie2026-lubin-1024x505.jpeg 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig5-IGNITE-spie2026-lubin-600x296.jpeg 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig5-IGNITE-spie2026-lubin-768x379.jpeg 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig5-IGNITE-spie2026-lubin-1536x757.jpeg 1536w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig5-IGNITE-spie2026-lubin-900x444.jpeg 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig5-IGNITE-spie2026-lubin.jpeg 1602w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n<p>For more details on the papers we presented, plus additional resources, visit <strong><a href=\"https:\/\/events.sw.siemens.com\/en-US\/calibre-spie\/\" target=\"_blank\" rel=\"noreferrer noopener\">Calibre IC Manufacturing papers at SPIE 2026.<\/a><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Nick Cobb memorial scholarship<\/h2>\n\n\n\n<p>Siemens EDA also partnered with SPIE to present the eighth annual <strong><a href=\"https:\/\/spie.org\/news\/2026-spie-nick-cobb-memorial-scholarship-recipient-announced\" target=\"_blank\" rel=\"noreferrer noopener\">Nick Cobb Memorial Scholarship<\/a><\/strong> to Shilong Zhang, a PhD student in electrical engineering at the Korea Advanced Institute of Science and Technology (KAIST) in Daejeon, South Korea.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"668\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig6-SPIE-ALP2026-Nick-Cobb.jpg\" alt=\"Presenters of the Nick Cobb Memorial Scholarship stand in front of a projection featuring Shilong Zhang, the winner of the scholarship. Photo courtesy of SPIE. Photographer: Ronald Goossens https:\/\/www.vibrantlightgallery.com\/Events\/Conferences\/SPIE-ALP-2026\" class=\"wp-image-3744\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig6-SPIE-ALP2026-Nick-Cobb.jpg 1000w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig6-SPIE-ALP2026-Nick-Cobb-600x401.jpg 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig6-SPIE-ALP2026-Nick-Cobb-768x513.jpg 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Fig6-SPIE-ALP2026-Nick-Cobb-900x601.jpg 900w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Presenters of the Nick Cobb Memorial Scholarship while featuring recipient Shilong Zhang. Photo courtesy of SPIE. Photographer: Ronald Goossens <a href=\"https:\/\/www.vibrantlightgallery.com\/Events\/Conferences\/SPIE-ALP-2026\" target=\"_blank\" rel=\"noopener\">https:\/\/www.vibrantlightgallery.com<\/a><\/figcaption><\/figure>\n\n\n\n<p>YuYang Sun, Vice President of product management for Siemens EDA&#8217;s Calibre Semiconductor Manufacturing Solutions, said \u201cZhang\u2019s doctoral research focuses on advancing next-generation mask technology for semiconductor lithography. Under the guidance of faculty in design and manufacturing, Zhang\u2019s work centers on developing machine-learning (ML)\u2013driven approaches to make curvilinear optical proximity correction (OPC) faster, more accurate, and practical for real manufacturing use\u2014making ML-guided curvilinear OPC a deployable, industry-ready technology. The scholarship honors Nick&#8217;s pioneering work in the field of lithography and his significant contributions as the chief architect of the Siemens EDA Calibre OPC solutions.&#8221;<\/p>\n\n\n\n<p>If you\u2019re interested in learning more about our work in any of these fields, let us know! Call us at 1-800-547-3000, or <strong><a href=\"https:\/\/www.siemens.com\/en-us\/contact\/\" target=\"_blank\" rel=\"noreferrer noopener\">send us a note<\/a><\/strong> telling us what topics you\u2019d like more information about.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Read about Calibre Manufacturing\u2019s highlights from the 2025 SPIE Advanced Lithography Patterning symposium. <\/p>\n","protected":false},"author":71645,"featured_media":3746,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1,5],"tags":[962,912],"industry":[],"product":[90],"coauthors":[712],"class_list":["post-3733","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-events","tag-semi-manufacturing","tag-spie","product-calibre"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/50\/2026\/03\/Blog-Hero-SPIE-ALP2026-900x414-1.jpg","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/posts\/3733","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/users\/71645"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/comments?post=3733"}],"version-history":[{"count":5,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/posts\/3733\/revisions"}],"predecessor-version":[{"id":3748,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/posts\/3733\/revisions\/3748"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/media\/3746"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/media?parent=3733"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/categories?post=3733"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/tags?post=3733"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/industry?post=3733"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/product?post=3733"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/calibre\/wp-json\/wp\/v2\/coauthors?post=3733"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}