NVIDIA says chipmakers are using CUDA-X libraries to bring AI into chip design and manufacturing
NVIDIA said companies such as Applied Materials, KLA and Teradyne use AI and simulation in production, and introduced the new cuPotential library.
On 14 October 2026, NVIDIA announced that semiconductor companies are adopting CUDA-X libraries for AI-driven manufacturing. It also opened early access to cuPotential, a library for building AI agents that run atomic-scale simulations.
NVIDIA said in a press release on 14 October 2026 that semiconductor companies are adopting CUDA-X libraries to move toward AI-defined fabs. NVIDIA uses the term for chip plants where AI supports engineers in tuning processes, fixing production problems and running lines more efficiently. The company also announced cuPotential, a new library that lets developers create AI agents for modelling interactions between atoms and molecules.
- According to NVIDIA, in early-access tests cuPotential made workflows that were already tuned for GPUs a further 2-3x faster.
- According to the release, Applied Materials uses NVIDIA tools to run chamber simulations up to 35x faster and to build fab digital twins.
- A new NPP image-processing kernel, shaped by lessons from KLA's own production kernels, runs 50x faster than earlier CPU versions.
- FANUC and Foxconn are connecting robot models to Omniverse for factory digital twins, and OMRON is developing agents for its inspection equipment that help engineers analyze circuit board results and look into possible defect causes.
Why it matters
The release shows AI being used not only in chip design but also for inspection, maintenance and process optimization on the production line. According to the release, roughly 85x faster probe-maintenance inference helps Teradyne catch degrading probes before they hurt yield, and Advantest uses agents to cut response times from hours to minutes. For electronics and industrial manufacturers in Türkiye, these examples show image-based quality control and digital twin applications at work in large production sites; similar methods could spread to smaller manufacturers over time. Before starting a similar project, assessing the quality of existing production and image data could be a sensible first step.
- Semiconductor companies are using CUDA-X libraries in production.
- The new cuPotential library is in early access.
- Image-based inspection and digital twin examples stand out.
Sources
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