NVIDIA says AI accelerates chip factory simulation by up to 35x

NVIDIA says AI is reshaping chip fabs: simulation up to 35x faster, engineers' decisions cut from days to minutes

2026-11-04 00:06 IT Home - Xiaobeng

IT Home, November 4: NVIDIA published an article on November 3 stating that artificial intelligence is pushing semiconductor fabs into the "AI-defined factory" stage: through accelerated simulation, learned physics models, AI agents and digital twins, it is significantly speeding up design, testing and production decisions in chip manufacturing.

Accelerated simulation refers to using GPUs, parallel computing, AI agent models and other technologies to compress engineering simulations that traditionally rely on CPUs from hours or days to minutes or even milliseconds. In semiconductor manufacturing, it lets engineers test process options and predict defect risks faster, without first running expensive and time-consuming real production-line experiments.

Citing the blog post, IT Home explains that as demand for advanced chips rises, manufacturing processes become increasingly complex, and the margin for error in yield and process quality keeps shrinking. Traditional production-line experiments are both slow and expensive, and engineers often need days to analyze process changes, material reactions or defect root causes. The value of AI lies in letting engineers compare options and spot risks faster in a virtual environment before committing to real experiments.

The goal of accelerated simulation is to obtain sufficiently reliable "virtual experiment" results faster before actual manufacturing, thereby reducing trial and error, improving yield and lowering costs.

Learned physics models are models that use machine learning to learn physical laws from high-precision simulations, experiments or production-line data. They learn the mapping relationships among variables such as temperature, pressure, materials, electromagnetic fields and plasma, thereby quickly predicting complex physical processes. In its semiconductor manufacturing report, NVIDIA regards them as one of the key technologies for accelerating fab simulation and decision-making.

A digital twin is a dynamic digital replica of a physical object, system or process created in a virtual environment. It is not just a static 3D model, but can receive real-time or historical data, simulate operating states, predict outcomes, and support a decision-making approach of "try first in the virtual world, then do it in the real world." In NVIDIA's report on semiconductor manufacturing, digital twins are used to build a virtual model of an entire fab, helping engineers identify bottlenecks and validate layout and operational strategies before production begins.

In specific cases, multiphysics simulation software COMSOL used NVIDIA cuDSS to speed up semiconductor manufacturing-related simulations by up to 10x; Siemens integrated cuLitho into its Calibre lithography software, speeding up key simulation workloads by up to 17x.

Applied Materials' Ginestra platform, after integrating cuDSS, is up to 10x faster than CPU-only solutions, and its ACE+ multiphysics platform achieved up to 35x faster topography simulation with PhysicsNeMo.

AI can also reduce repeated simulations. For example, modeling plasma behavior used in semiconductor etching is complex and costly; Lam Research showed that PhysicsNeMo can predict plasma behavior in milliseconds, whereas a full simulation may take days.

In the past, to determine whether a chip manufacturing plan was feasible, engineers had to wait for lengthy simulations, experiments or data analysis; with AI-accelerated simulation, learned physics models and AI agents, many analysis tasks that originally took "days" can now be completed in "minutes."

NVIDIA cites Advantest as an example in the report: Advantest uses NeMo Switchyard AI agents to automatically select appropriate AI models, compressing tasks that originally took hours into minutes.

In advanced fabs, the questions engineers face are often not simple data queries; they usually need to determine:

The traditional process usually requires high-precision physical simulation, result analysis and option comparison in sequence, and sometimes actual experiments must be arranged. A complete simulation may take days, making it difficult for engineers to quickly explore a large number of alternatives.

AI can first help engineers narrow the scope and identify high-risk options, while key process changes still require high-precision simulation, experimental validation and professional review by engineers. AI frees engineers from "long waits for calculation results," allowing them to spend time on judgment, validation and optimization; waiting time in the decision chain is shortened, but professional responsibility and final gatekeeping remain in the hands of engineers.

An intuitive analogy: in the past, a doctor had to wait for a full set of test reports before making a preliminary judgment; now AI is like quickly providing multiple screening results and possible causes first, so the doctor can immediately decide which key tests to prioritize. It is faster, but the final diagnosis and treatment decisions are still the doctor's responsibility.

More importantly, Applied Materials is building a digital twin of an entire fab based on NVIDIA Omniverse, which can simulate material flow, identify bottlenecks and validate layout and operational strategies in a virtual environment before deciding whether to implement them on the real production line.

Applied Materials uses digital twin technology built on NVIDIA Omniverse libraries to simulate factory-level layout, throughput, cost and process control optimizations.

NVIDIA says this closed loop will enable factories to learn from each production cycle and act earlier; engineers remain responsible for professional judgment and final decisions, but the time spent waiting for answers will be greatly reduced.

A realistic digital twin model of a fab built with NVIDIA Omniverse libraries, used to simulate material flow, identify bottlenecks and validate operational strategies before deployment. Image courtesy of Applied Materials.