News Summary:<\/p>\n
\n\tNVIDIA CUDA-X libraries and AI models are accelerating TSMC workloads across lithography, transistor and process simulation, advanced process control and fab operations optimization.
\n\tTSMC is using NVIDIA Metropolis and NVIDIA TAO Toolkit to advance automated defect inspection with vision AI, improving detection of nanometer-scale defects while reducing repeated labeling and retraining.
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NVIDIA GTC Taipei\u2014NVIDIA today announced that TSMC, the world\u2019s leading semiconductor company, is using NVIDIA accelerated computing and AI to advance semiconductor design and manufacturing.<\/p>\n
As chips move to more advanced nodes, bringing them from design to high-volume production has become one of the world\u2019s most complex computing challenges. Computational lithography, transistor simulation, process control and wafer inspection now require massive-scale simulation and real-time optimization, and AI systems that can provide support across physics, images and other applications.<\/p>\n
TSMC is using NVIDIA technologies to accelerate this transformation, applying accelerated computing and AI across the semiconductor design and manufacturing<\/a> lifecycle to improve turnaround time, energy efficiency, yield and operational productivity in advanced fabs.<\/p>\n \u201cNVIDIA and TSMC have worked together for nearly three decades to push the limits of computing,\u201d said Jensen Huang, founder and CEO of NVIDIA. \u201cTSMC is bringing NVIDIA AI and accelerated computing into the fab itself, tackling some of the world\u2019s most complex design and manufacturing challenges with simulation, optimization and AI to improve speed, efficiency and yield for the next generation of chips.\u201d<\/p>\n \u201cTSMC and NVIDIA have built a long-standing partnership rooted in advancing the technologies that make the next generation of computing possible,\u201d said C.C. Wei, chairman and CEO of TSMC. \u201cBy using NVIDIA accelerated computing and AI across fab operations optimization, lithography, process control and inspection, TSMC is strengthening our technology leadership and manufacturing excellence to support our customers\u2019 future products and success.\u201d<\/p>\n TSMC Accelerates Processes With NVIDIA CUDA-X Libraries and AI TSMC is using NVIDIA CUDA-X<\/a>\u2122 libraries and AI models to accelerate these workloads on NVIDIA GPUs:<\/p>\n
TSMC Advances Defect Inspection With NVIDIA Metropolis and AI Models TSMC is using the NVIDIA Metropolis<\/a> platform and NVIDIA TAO Toolkit<\/a> to improve advanced defect classification. Using vision AI, TSMC has improved detection of defects at nanometer scale.<\/p>\n These capabilities help TSMC improve quality inspection while reducing the need for repeated labeling and retraining as process conditions, inspection tools and defect types change.<\/p>\n TSMC Taps NVIDIA Omniverse to Build FabTwin TSMC is exploring NVIDIA Omniverse\u2122 libraries to build FabTwin, a virtual fab environment for evaluating process tool layouts and related simulation workflows. By testing design scenarios digitally before physical implementation, TSMC can compare complex configurations more flexibly and identify potential constraints earlier. This virtual-first approach vastly improves planning efficiency and accelerates critical decision-making before any physical or capital commitments are made.<\/p>\n Watch Huang\u2019s keynote<\/a> and learn more at NVIDIA GTC Taipei<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"News Summary:
NVIDIA CUDA-X libraries and AI models are accelerating TSMC workloads across lithography, transistor and process…\n","protected":false},"author":2,"featured_media":470294,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[345,343,344,85,46,125],"class_list":["post-470293","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-il","tag-israel","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts\/470293","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/comments?post=470293"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts\/470293\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/media\/470294"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/media?parent=470293"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/categories?post=470293"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/tags?post=470293"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nAdvanced semiconductor design and manufacturing require massive computational workloads and highly coordinated fab operations, spanning chip-design transfer, transistor modeling, process control and fab productivity.<\/p>\n
\n\tComputational lithography: TSMC is using NVIDIA cuLitho<\/a>, a GPU-accelerated library for lithography \u2014 a printing method for chip mask design. This technology delivers a 20-50% improvement in cost effectiveness or cycle time compared with CPU-based computational lithography, while maintaining the same cost of ownership.
\n\tTransistor, equipment and process simulation: TSMC is using NVIDIA cuEST<\/a>, a GPU-accelerated electronic structure simulation library for 50x faster chemistry simulations, on average, for semiconductor material design.
\n\tAdvanced process control: TSMC is using the NVIDIA cuML<\/a> machine learning library to accelerate large-scale analytics on NVIDIA GPUs. This lets TSMC speed algorithms and distill hundreds of thousands of process parameters spanning thousands of steps as precision inputs for machine learning models \u2014 making significant reduction in process variation.
\n\tFab operations optimization: GPU-accelerated scheduling computation using CUDA has led to notable improvements in fab productivity with NVIDIA H200 GPUs<\/a>. By harnessing CUDA-powered computation on NVIDIA H200 GPUs, TSMC has enhanced its capability to manage complex constraints, thereby streamlining production paths and maximizing fab productivity.
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\nAs chips become more advanced, even the smallest defects can affect quality and yield, making faster and more accurate inspection essential to semiconductor design and manufacturing.<\/p>\n
\nAdvanced semiconductor fabs are among the most complex fabs ever built, requiring precise coordination across tools, materials, robots, humans and facility systems.<\/p>\n