NVIDIA today announced semiconductor leaders are adopting NVIDIA CUDA-X libraries — including the new NVIDIA cuPotential library, which developers can use to build AI agents that simulate how atoms and molecules interact — to advance AI-defined fabs. AI-defined fabs are semiconductor factories where AI helps engineers optimize processes, solve production issues and improve production efficiency. As demand for advanced chips rises, such fabs must expand production while managing increasingly complex manufacturing processes. Engineers need new ways to model materials, analyze inspection data and test process changes in digital twins before applying them in production. The expanded CUDA-X portfolio gives AI agents and engineering applications accelerated tools for semiconductor simulation, 3D data processing and image analysis, helping automate more engineering work across chip design and manufacturing so fabs can predict process behavior and improve yield. “The semiconductor industry made the AI revolution possible. Now, AI is transforming how chips are built,” said Tim Costa, vice president and general manager of industrial and computational engineering at NVIDIA. “As fabs race to meet demand for advanced chips, NVIDIA CUDA-X and Omniverse libraries give AI agents accelerated simulation and inspection tools to help engineers manage complexity, improve yield and advance AI-defined fabs.” NVIDIA cuPotential Accelerates How Researchers Model Atomic Interactions Understanding how atoms and molecules interact is critical to modeling semiconductor materials and manufacturing processes, but these calculations can take significant amounts of time, even in software already optimized for GPUs. NVIDIA cuPotential accelerates atomic-scale simulation with specialized GPU-accelerated functions developers can add to existing software. It helps researchers model electrical forces and other interactions across large numbers of atoms to test and refine simulations faster. Synopsys is among the early adopters using cuPotential to accelerate materials simulations. By making these modeling capabilities available to AI agents, cuPotential helps agents run, evaluate and iterate on advanced molecular simulations. In early-access testing, the library accelerated already GPU-optimized workflows by an additional 2-3x. Semiconductor Leaders Advance AI-Defined Fabs With NVIDIA CUDA-X AI-defined fabs require faster ways to model chip designs, materials behavior and manufacturing processes before changes reach production. NVIDIA CUDA-X libraries — including NVIDIA cuPotential for simulating atomic interactions, NVIDIA cuEST for modeling electronic behavior and NVIDIA cuDSS for solving complex engineering simulations — accelerate these workloads to help make fabs more autonomous. Synopsys is using cuPotential, cuEST and cuDSS to accelerate semiconductor simulations, with early QuantumATK results showing a potential 30x speedup using cuEST, and S-Device benchmarks showing a 12x speedup using cuDSS. The company’s AgentEngineer solutions interoperate with NVIDIA Nemotron ™ open models and NVIDIA NeMo ™ to support AI agents for chip design and verification. Cadence is using NVIDIA accelerated computing, NVIDIA Nemotron open models and CUDA-X libraries to bring agentic AI to the design and simulation of electromechanical systems — from semiconductors to advanced packaging and printed circuit boards. Examples include Cadence AuraStack AI Super Agent optimizing advanced packages for heat and stress 8x faster using cuDSS with Cadence Celsius, and Nemotron with ChipStack AI Super Agent for semiconductor design and verification. COMSOL is accelerating semiconductor multiphysics simulations with NVIDIA cuDSS, delivering up to 10x faster simulations to help engineers optimize equipment and processes. Its upcoming COMSOL MCP Server will use the NVIDIA Nemotron Ultra open model to enable AI-assisted modeling and design exploration. Voltai accelerates advanced chip packagin