Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing

Applied Materials and NVIDIA are collaborating on an end-to-end digital development model that combines Applied Materials' materials engineering and semiconductor manufacturing expertise with NVIDIA CUDA-X libraries to accelerate semiconductor innovation. By integrating NVIDIA cuDSS into Applied Materials' Ginestra simulation platform, the companies achieved up to a 10x speedup over CPU-only approaches for atomic-scale materials modeling, enabling engineers to run thousands of virtual experiments. The partnership also leverages NVIDIA cuEST to accelerate density functional theory calculations, addressing compute-intensive challenges in advanced semiconductor development.

As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have outsized financial impact in fast-moving AI hardware cycles. Simultaneously, the shift from chip-level optimization to system-level engineering is compounding thermal and power challenges. Meeting these demands requires breakthroughs in materials deep within the device stack. This calls for advanced modeling and simulation beyond what conventional approaches can deliver. This post explores how Applied Materials https://www.appliedmaterials.com/us/en.html and NVIDIA are tackling this challenge with an end-to-end digital development model that combines Applied Materials leadership in materials engineering and semiconductor manufacturing with NVIDIA CUDA-X libraries https://developer.nvidia.com/cuda/cuda-x-libraries . This collaboration accelerates the entire innovation pipeline—from atomic-scale discovery and engineering through process development to factory optimization. How are NVIDIA and Applied Materials accelerating semiconductor innovation? At the front end, GPU-accelerated simulations expand the design space, giving materials engineers a powerful advantage in developing next-generation devices. In the middle, physics-based modeling speeds up chamber and recipe development. At the back end, AI-driven digital twins https://www.nvidia.com/en-us/glossary/digital-twin/ predict fab performance before changes hit the production floor. Putting these together creates a continuous flow of insights that drives faster progress from atoms to fabs. Advancing atomic-scale materials modeling As geometric scaling slows, progress in advanced semiconductors increasingly hinges on materials innovation in dielectrics, conductors, and other thin films. At atomic levels, even minor variations in material structure and interfaces can significantly impact performance, reliability, and yield. Identifying these effects through physical experimentation alone is too slow and costly. Applied Materials addresses this constraint with Ginestra, a physics-based, defect-centric simulation platform that connects material properties and defects to predicted device performance. One key application is the high-k metal gate HKMG stacks in gate-all-around GAA transistors and other advanced 3D devices, which pack five distinct materials into a space barely 1/10,000th the width of a human hair. By combining metrology data with first-principles simulation, Ginestra projects device variability and reliability directly from atomic-scale material characteristics. Historically, achieving the required simulation fidelity needed significant compute time, restricting how broadly engineers could explore the materials landscape. By integrating NVIDIA cuDSS https://developer.nvidia.com/cudss CUDA-X Direct Sparse Solver into Ginestra, Applied Materials accelerates the sparse linear algebra at the core of these simulations, delivering up to a 10x speedup over CPU-only approaches without sacrificing accuracy. Engineers can now run thousands of virtual experiments across material combinations and reaction pathways, dramatically expanding the pool of viable candidates. While this broadens the search space, accurate atomic-level prediction also depends on density functional theory DFT , the industry-standard method for modeling electronic structure. DFT is highly predictive but also highly compute-intensive. Traditional CPU-based simulations can take days or even weeks, limiting DFT industrial scalability. NVIDIA cuEST https://developer.nvidia.com/cuda/cuda-x-libraries/cuest CUDA Electronic Structure Theory accelerates the most demanding steps of the DFT workflow. cuEST is one of the most recent CUDA-X libraries offered by NVIDIA to accelerate quantum chemistry applications on GPUs. Its modular APIs accelerate the most computationally demanding building blocks of Gaussian-basis density functional theory while integrating with existing quantum chemistry workflows. On NVIDIA B200 systems, simulations that once took five days on 64 CPU cores can now be completed in about two hours on a single GPU—an approximately 55x speedup. Even complex reaction-search tasks that previously required hundreds of CPU cores for over a week can now run in hours. Together, Ginestra workflows and CUDA-X shift more materials engineering into simulation. This helps engineers expand exploration, reduce reliance on costly experiments, and accelerate discovery of optimal materials. Bringing materials innovation to high-volume manufacturing After identifying promising materials, the next challenge is translating those insights into repeatable, high-yield manufacturing processes governed by tightly coupled physics—fluid flow, heat transfer, plasma dynamics, surface reactions, and electromagnetic effects. The Applied Materials ACE+ platform brings these multiphysics models together to simulate how chamber design, hardware configuration, and process parameters interact. NVIDIA PhysicsNeMo https://developer.nvidia.com/physicsnemo then converts simulation results into real-time digital twins of process behavior. With GPU acceleration, ACE+ topography simulations run up to 35x faster—compressing workflows that once took days into same-day results. Engineers can dynamically explore parameter changes and see instant impact, shifting from batch-based iteration to a continuous, interactive workflow that converges on optimized conditions much earlier. Validated process innovations are then deployed into production using Applied Materials Integrated Materials Solutions platforms. One example is Endura, the semiconductor industry’s most successful metallization system, combining multiple process steps under vacuum to deliver the precision required for advanced chipmaking. From there, the focus expands to the full fab environment, where tool interactions and workflow dynamics ultimately determine throughput, cost, and yield. At the factory scale, Applied Materials leverages NVIDIA Omniverse https://www.nvidia.com/en-us/omniverse/ libraries to build physically accurate digital twins of entire fabs. These environments allow teams to optimize layouts, simulate and streamline material flow, identify bottlenecks, and validate operational strategies before deployment—accelerating ramp to high-volume manufacturing. Learn more To meet the growing compute demands of the AI era, the semiconductor industry must embrace a connected model spanning atomic-scale materials engineering, process development, and manufacturing optimization. By combining Applied Materials’ semiconductor fabrication expertise with NVIDIA CUDA-X accelerated simulation, engineers can tackle critical bottlenecks faster and speed up the path to next-generation technologies. More broadly, this reflects the industry’s shift toward cross-ecosystem innovation—exemplified by initiatives like Applied’s EPIC platform—where progress increasingly comes from closer collaboration across the semiconductor value chain. cuEST https://developer.nvidia.com/cuda/cuda-x-libraries/cuest advances high-accuracy electronic-structure modeling, cuDSS https://developer.nvidia.com/cudss accelerates large-scale engineering simulations, NVIDIA PhysicsNeMo https://developer.nvidia.com/physicsnemo transforms simulation data into real-time digital twins, and NVIDIA Omniverse connects data, models, and workflows through physically based digital twins. Together, these technologies create a continuous digital thread across the semiconductor innovation lifecycle—accelerating the development of the foundational technologies that will power the future of AI. Learn more about accelerating semiconductor design and manufacturing https://www.nvidia.com/en-us/industries/semiconductor/ .