# Accelerating Semiconductor Design and Manufacturing Powering semiconductor breakthroughs with AI, digital twins, and accelerated computing. [Watch Keynote](https://www.youtube.com/watch?v=7KxVR53PWMw&t=1341s) Image courtesy of TSMC Introduction ## Accelerated Computing and AI for Semiconductor Workflows Shrinking nodes, 3D integration, and chiplet architectures are pushing compute demands beyond what CPU-based tools can sustain. Fabs and OSATs face the same pressure from the manufacturing side, needing higher throughput, better yields, and tighter process control at every node. NVIDIA platforms combine GPUs, [NVIDIA Vera CPUs](https://www.nvidia.com/en-us/data-center/vera-cpu.md), high-speed interconnects, [NVIDIA CUDA-X](https://www.nvidia.com/en-us/technologies/cuda-x.md)™ and [NVIDIA Omniverse](https://developer.nvidia.com/omniverse?size=n_12_n&sort-field=featured&sort-direction=desc)™ libraries to accelerate computing, power AI, and real time digital twins, across the full semiconductor workflow, from design and verification to fab operations, inspection, and test. ### Semiconductor Industry Accelerates Design and Manufacturing With NVIDIA See how TSMC, Cadence, KLA, Siemens, Synopsys, and others are using NVIDIA Blackwell, Grace, and CUDA‑X libraries like cuLitho and cuDSS to accelerate EDA, lithography, and process simulation across advanced nodes. [Read Blog](https://blogs.nvidia.com/blog/semiconductor-industry-electronic-design-automation-blackwell-cuda-x/) ### Optimizing Semiconductor Defect Classification With Generative AI Learn how generative AI and vision foundation models running on NVIDIA platforms can improve defect detection, boost classification accuracy, and help scale inspection across high‑volume manufacturing. [Read Developer Blog](https://developer.nvidia.com/blog/optimizing-semiconductor-defect-classification-with-generative-ai-and-vision-foundation-models/) ### Use Cases ## Semiconductor Use Cases: Accelerated Computing, Engineering With AI, and Physical AI From design and verification to lithography, fab optimization, inspection, and test, NVIDIA powers critical semiconductor workflows with full‑stack acceleration. #### Accelerated EDA Design space is infinite. Schedules aren’t. GPU-accelerated EDA compresses the path from RTL to sign-off so teams explore more and respin less. [Learn More About AI-Powered EDA](https://blogs.nvidia.com/blog/cadence-millennium-nvidia-blackwell/) #### Lithography Days of mask optimization, compressed to hours. cuLitho brings full-chip lithography simulation to GPUs so teams catch patterning issues earlier and ramp to yield faster. [Learn About cuLithio](https://developer.nvidia.com/culitho) #### The Age of the Autonomous Engineer AI super agents autonomously execute chip design verification, with no handoff, compressing weeks of expert engineering work into hours. [Watch Now](https://youtu.be/k0Rgc3ZH5Co?si=V-FWz4763YXpIQnp) Cadence #### Accelerating Reactor Simulation with AI Physics Modeling etch and deposition reactors involves coupled physics so complex that a single simulation can take days. NVIDIA PhysicsNeMo™ surrogate models evaluate the same workflows in milliseconds, accelerating chamber design and process. [Learn About PhysicsNeMo](https://developer.nvidia.com/physicsnemo) #### Fab Operations Gigafab lot scheduling demands real-time decisions across thousands of tools. GPU-accelerated scheduling delivers the compute speed modern fab operations require. [Get Started With cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt/get-started.md) #### Wafer Inspection Advanced vision AI agents analyze live floor data to optimize quality, safety, and productivity. By reasoning through process deviations, these intelligent systems mitigate yield and capital risk in real time. [Watch Ai Agent Video](https://youtu.be/HCh3nOLuUCA?si=t1fCY4MVXwA3IwrQ) ### Success Stories ## Semiconductor Success Stories and Real-World Impact Learn how leading semiconductor innovators use NVIDIA-accelerated computing to boost yield, increase throughput, and cut costs. Image courtesy of Alsemy ### Alsemy Accelerates Chip Modeling From Months to Minutes With Physics-Informed AI Alsemy is transforming semiconductor device modeling by replacing slow, manual workflows with physics-informed AI on NVIDIA RTX™ GPUs and PhysicsNeMo. [Read Alsemy Case Study](https://www.nvidia.com/en-us/case-studies/alsemy.md) Image courtesy of MediaTek ### MediaTek Accelerates AI Development With an AI Factory MediaTek is building an on-premises AI factory powered by NVIDIA DGX SuperPOD™ to accelerate large language model development and enterprise AI. [Read MediaTek Case Study](https://www.nvidia.com/en-us/case-studies/mediatek-ai-factory.md) ### News and Events ## Latest News and Events for Semiconductor Industries TSMC ### NVIDIA and TSMC Bring AI Into Fabs to Advance Semiconductor Design and Manufacturing The world’s leading semiconductor company, TSMC, is using NVIDIA accelerated computing and AI to advance semiconductor design and manufacturing. [Read News](https://nvidianews.nvidia.com/news/nvidia-and-tsmc-bring-ai-into-fabs-to-advance-semiconductor-design-and-manufacturing) ### Semiconductor Industry Accelerates Design Manufacturing With NVIDIA Blackwell and CUDA-X TSMC, Cadence, KLA, Siemens, and Synopsys are advancing semiconductor manufacturing by adopting the NVIDIA CUDA-X and NVIDIA Blackwell platforms. [Read Blog](https://blogs.nvidia.com/blog/semiconductor-industry-electronic-design-automation-blackwell-cuda-x/) ### NVIDIA and Samsung Build AI Factory to Transform Global Intelligent Manufacturing New AI factory with 50,000 NVIDIA GPUs to accelerate agentic and physical AI applications for advanced chip manufacturing, mobile devices, and robotics. [Read Press Release](https://nvidianews.nvidia.com/news/samsung-ai-factory) ### Technology ## Full‑Stack Acceleration for Semiconductor Workflows Semiconductor workloads demand high performance, massive scale, and tight integration across tools, data, and infrastructure. NVIDIA delivers a full‑stack platform for core workload acceleration, engineering with AI software, and real-time digital twins built for compute‑intensive design, simulation, and manufacturing. ### Core Workload Acceleration Leveraging NVIDIA CUDA-X, [cuDSS](https://developer.nvidia.com/cudss), and foundational solver libraries for EDA acceleration, alongside domain-specific tools like [cuLitho](https://developer.nvidia.com/culitho) for lithography and [cuEST](https://developer.nvidia.com/cuda/cuda-x-libraries/cuest) for atomic-level modeling [Explore Data Center Solutions](https://www.nvidia.com/en-us/data-center.md) ### Engineering With AI Accelerate semiconductor innovation with [AI physics](https://developer.nvidia.com/physicsnemo) for process simulation, [agentic AI](https://www.nvidia.com/en-us/solutions/ai/agentic-ai.md) for autonomous design, and [physical AI](https://www.nvidia.com/en-us/glossary/generative-physical-ai.md) for intelligent fab automation. Applied Materials ### Real-Time Digital Twins Use NVIDIA Omniverse digital twins to simulate fabs, optimize operations, and orchestrate smarter semiconductor factories. [Learn About Omniverse](https://www.nvidia.com/en-us/omniverse.md) ## FAQs About NVIDIA Solutions for Semiconductor Industries ### What does EDA stand for, and how is it accelerated by NVIDIA? Electronic Design Automation (EDA) is the category of software tools used to design and verify integrated circuits. NVIDIA accelerates EDA by combining GPUs and CUDA-X libraries with tools from partners like Cadence and Synopsys to dramatically compress the time required to move from RTL to chip sign-off. * Read more about how [AI-Powered EDA and Blackwell accelerate design processes](https://blogs.nvidia.com/blog/cadence-millennium-nvidia-blackwell/). * Explore the latest breakthroughs in [accelerated computing for the semiconductor industry](https://blogs.nvidia.com/blog/semiconductor-industry-electronic-design-automation-blackwell-cuda-x/). ### What is cuLitho, and how does it revolutionize computational lithography? cuLitho is an NVIDIA CUDA-X library designed to accelerate computational lithography—the process of optimizing photomasks to counteract patterning issues during chip manufacturing. By moving full-chip lithography simulation to GPUs, cuLitho compresses what historically took days of computation into a matter of hours, enabling faster time-to-yield. * Visit the dedicated [NVIDIA cuLitho developer page](https://developer.nvidia.com/culitho) for technical documentation. ### What is cuDSS, and what is its role in core EDA acceleration? cuDSS is a foundational solver library within the NVIDIA CUDA-X platform, designed to provide core workload acceleration for EDA and other computationally-intensive processes. It delivers highly optimized solvers for tasks that are critical to electronic design automation tools. * Find technical resources and support for [CUDA-X libraries like cuDSS](https://developer.nvidia.com/cudss). ### What is cuEST, and for which semiconductor workflow is it designed? cuEST is a component of the NVIDIA CUDA-X libraries used for atomic-level modeling and simulation in the semiconductor workflow. It provides acceleration for highly detailed simulations required in materials science and process engineering. * Explore the specific capabilities of the [cuEST acceleration library](https://developer.nvidia.com/cuda/cuda-x-libraries/cuest) on the NVIDIA Developer site. ### What are NVIDIA PhysicsNeMo™ surrogate models, and what problem do they solve in manufacturing? PhysicsNeMo are AI surrogate models that use AI physics to accelerate reactor simulation. They replace computationally complex, multi-day simulations of etch and deposition reactors—which involve coupled physics—with evaluations that take milliseconds, significantly accelerating chamber design and process development. * Learn about accelerating simulations with [NVIDIA PhysicsNeMo AI Physics](https://developer.nvidia.com/physicsnemo) models. ### How is NVIDIA Omniverse™ utilized to create real-time digital twins in semiconductor factories? NVIDIA Omniverse is a platform used to build and operate real-time digital twins. In the semiconductor industry, it simulates entire fabs and manufacturing facilities to optimize operations, test new workflows, and orchestrate smarter factories by creating a virtual replica of the physical world. * Discover how the [NVIDIA Omniverse platform](https://www.nvidia.com/en-us/omniverse.md) is used for industrial digitization and simulation. ### What are the core technology components that make up the NVIDIA full-stack platform for semiconductor workflows? The NVIDIA full-stack platform combines GPUs (such as Blackwell), NVIDIA Vera CPUs, high-speed interconnects, and a software layer that includes the NVIDIA CUDA-X™ libraries (like cuLitho, cuDSS, and cuEST) and the NVIDIA Omniverse™ platform for digital twins. * Learn about the foundational elements of the [NVIDIA platform for data centers](https://www.nvidia.com/en-us/data-center.md) and accelerated computing. ### Which specific NVIDIA software components are used for Core Workload Acceleration? Core Workload Acceleration leverages the NVIDIA CUDA-X platform, including foundational solver libraries like cuDSS, as well as domain-specific tools such as cuLitho for lithography and cuEST for atomic-level modeling. * Explore the available [CUDA-X libraries and technologies](https://www.nvidia.com/en-us/technologies/cuda-x.md) for scientific and engineering acceleration. ### Which leading EDA and manufacturing companies are partnering with NVIDIA on accelerated computing? Major partners span the entire workflow, including EDA leaders like Cadence and Synopsys, manufacturing equipment suppliers such as Lam Research, and chipmakers like TSMC and Samsung, all of whom are leveraging NVIDIA's platforms to accelerate their processes. * Read the blog post detailing how [leading semiconductor companies are adopting NVIDIA technologies](https://blogs.nvidia.com/blog/semiconductor-industry-electronic-design-automation-blackwell-cuda-x/). * Review the case study on how [Alsemy accelerates chip modeling using Physics-Informed AI](https://www.nvidia.com/en-us/case-studies/alsemy.md). ### What is the practical difference between agentic AI and physical AI in a fab environment? Agentic AI refers to the autonomous software agents used primarily in the design and verification stages (e.g., executing chip design tasks), while physical AI is used for intelligent automation in the manufacturing process itself, leveraging tools and sensors to optimize quality and productivity on the factory floor. * Learn more about the components and uses of [agentic AI](https://www.nvidia.com/en-us/solutions/ai/agentic-ai.md) in complex workflows. * Understand the function and implementation of [generative physical AI](https://www.nvidia.com/en-us/glossary/generative-physical-ai.md) for industrial automation. ## Partners ## Semiconductor Partner Ecosystem ## Resources ## Semiconductor Industry Resources 1. Videos 2. Sessions ### AI Supercomputing for Next-Generation Semiconductor Design and Manufacturing Watch Tim Costa’s SEMICON West 2025 keynote on AI supercomputing driving next-generation semiconductor design and manufacturing. [Watch Keynote](https://www.youtube.com/watch?v=7KxVR53PWMw) ### AI Agents for Visual Inspection in Manufacturing Visual AI agents are transforming semiconductor manufacturing by automating visual inspection and boosting quality and productivity. [Watch Video](https://www.youtube.com/watch?v=HCh3nOLuUCA) ### Applied Materials Accelerates Chip Manufacturing With NVIDIA See how Applied Materials and NVIDIA use digital twins built on NVIDIA Omniverse to optimize fab layout, throughput, cost, and process control. [Watch Video](https://www.youtube.com/watch?v=8htj3dn8EFI) ## Get Started ### Stay up to date on NVIDIA Semiconductor News [Subscribe](#subscribe) ### NVIDIA Developer Program Join the NVIDIA Developer Program for access to free SDKs, support, and tech resources. Gain early access to NVIDIA technology, support from forums, and expert training. [Join the Developer Program](https://developer.nvidia.com/developer-program) ## Get the Latest News Welcome back. Not you? 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