IT Home, November 4: On November 3, NVIDIA published an article stating that AI is driving semiconductor fabs into the "AI-defined factory" era—significantly accelerating design, testing, and production decisions in chip manufacturing through accelerated simulation, learning-based physical models, AI agents, and digital twins.
Accelerated simulation refers to the process of reducing traditional CPU-dependent engineering simulations—from hours or days—to minutes or even milliseconds by leveraging technologies such as GPUs, parallel computing, and AI agent models. In semiconductor manufacturing, it enables engineers to rapidly test process designs and predict defect risks without first conducting costly and time-consuming real-world production line experiments.
IT Home, citing a blog post, notes that as demand for advanced chips rises and manufacturing processes become increasingly complex, the margin for error in yield and process quality continues to shrink. Traditional production line experiments are both slow and expensive, often requiring engineers days to analyze process changes, material reactions, or root causes of defects. The value of AI lies in enabling engineers to quickly compare solutions and identify risks in a virtual environment before committing to real-world trials.
The goal of accelerated simulation is to obtain sufficiently reliable "virtual experiment" results faster than real manufacturing, thereby reducing trial and error, improving yield, and lowering costs.
Learned physics models are models that use machine learning to discover physical laws from high-fidelity simulations, experiments, or production line data. They learn the mapping relationships between variables such as temperature, pressure, materials, electromagnetic fields, and plasma, enabling rapid prediction of complex physical processes. NVIDIA has identified them as one of the key technologies for accelerating factory simulation and decision-making in semiconductor manufacturing.
A digital twin is a dynamic digital replica of a physical object, system, or process created in a virtual environment. It is not merely a static 3D model, but rather one that receives real-time or historical data, simulates operational states, predicts outcomes, and supports decision-making through the approach of “test first in the virtual world, then implement in the real world.” In NVIDIA’s coverage of semiconductor manufacturing, digital twins are used to build virtual models of entire wafer fabs, enabling engineers to identify bottlenecks, validate layouts, and test operational strategies before production begins.
In specific cases, the multiphysics simulation software COMSOL achieved up to a 10x speedup in semiconductor manufacturing simulations by leveraging NVIDIA cuDSS, while Siemens integrated cuLitho into its Calibre lithography software, achieving up to a 17x speedup in key simulation workloads.
Applied Materials' Ginestra platform achieves up to 10x faster performance when integrated with cuDSS compared to CPU-only solutions, and its ACE+ multiphysics platform realizes up to 35x faster topology simulation acceleration with PhysicsNeMo.
AI can also reduce redundant simulations. For example, modeling plasma behavior used in semiconductor etching is complex and costly; Lam Research demonstrated that PhysicsNeMo can predict plasma behavior in milliseconds, whereas full simulations may take days.
Previously, engineers had to wait extended periods for simulations, experiments, or data analysis to determine whether a chip manufacturing process was feasible. With the introduction of AI to accelerate simulations, learning-based physical models, and AI agents, many analysis tasks that once took days can now be completed in minutes.
NVIDIA cited in the report that Advantest uses the NeMo Switchyard AI agent to automatically select the appropriate AI model, reducing tasks that previously took hours to just minutes.
In advanced semiconductor fabs, engineers often face issues that require more than simply querying data—they typically need to make judgments:
- Will certain etching parameters damage the wafer?
- Will a change in materials or process affect yield?
- Whether the defect originates from the equipment, materials, or process conditions;
- Among the multiple candidate solutions, which one is most worth validating on a live production line?
Traditional processes typically require high-precision physical simulations, followed by result analysis and design comparisons, and sometimes even physical testing. A single full simulation can take several days, making it difficult for engineers to quickly explore a large number of alternative designs.
AI can first assist engineers in narrowing down options and identifying high-risk solutions; critical process changes still require high-precision simulation, experimental validation, and professional review by engineers. AI frees engineers from “waiting extended periods for computation results,” allowing them to focus their time on judgment, verification, and optimization. While waiting times in the decision-making chain are reduced, professional responsibility and final approval remain with the engineers.
An intuitive analogy is that in the past, doctors had to wait for all test results before making an initial diagnosis; now, AI quickly provides multiple screening results and possible causes, allowing doctors to immediately decide which key tests to prioritize. The process is much faster, but the final diagnosis and treatment decisions still rest with the doctor.
More importantly, Applied Materials is building a digital twin of an entire semiconductor fab based on NVIDIA Omniverse, enabling simulation of material flow, identification of bottlenecks, and validation of layout and operational strategies in a virtual environment before deciding whether to implement them on the actual production line.
NVIDIA says this closed loop will enable factories to learn from each production cycle and act earlier; engineers will still be responsible for expert judgment and final decisions, but the wait time for answers will be significantly reduced.