And digital twins let teams test changes in a virtual environment before committing to physical trials.
Together, these capabilities describe an AI-defined fab: a semiconductor manufacturing factory where AI helps engineers optimize processes, catch problems earlier and make better decisions across the full manufacturing chip lifecycle. Engineers direct the work and make decisions based on their expertise while spending less time waiting for answers.
Accelerated computing doesn’t just speed up individual calculations — it changes how many alternatives engineers can test before committing.
Multiphysics simulations model interactions among physical effects, such as heat transfer, electrical behavior and mechanical stress.
Using the NVIDIA cuDSS library, multiphysics software provider COMSOL is accelerating such simulations for semiconductor manufacturing by up to 10x. This helps engineers optimize equipment and processes, compare alternatives and decide which changes to test physically.
Applied Materials and NVIDIA are collaborating to accelerate atomic-scale materials discovery and process development. By integrating NVIDIA cuDSS, Applied’s Ginestra simulation platform runs up to 10x faster than CPU-only approaches. And with NVIDIA PhysicsNeMo, Applied’s ACE+ multiphysics platform delivers topography simulations up to 35x faster.
In some cases, acceleration doesn’t just shorten the calculation — it enables it altogether. Computational lithography — which defines how circuit patterns are printed onto silicon — involves calculations too intensive to complete on CPU at production scale. Siemens is integrating NVIDIA cuLitho into its Calibre lithography software, delivering up to 17x acceleration on key simulation workloads. That expands what engineers can verify before a wafer goes to print.
As such, faster calculations can help researchers explore material behavior and reaction pathways that influence next-generation semiconductor processes.
In addition to accelerating simulations, AI trained on physics can predict how materials and processes will behave — without running a full simulation each time.
The payoff is most visible in complex calculations that are expensive even on accelerated hardware.
Plasma — the electrically charged gas used in semiconductor etch processes — is one example. Modeling plasma behavior requires solving equations that capture how electrical, chemical and thermal processes interact, making repeated simulation costly. Lam Research demonstrated that the NVIDIA PhysicsNeMo framework can predict plasma behavior in milliseconds, compared with days for a full simulation — giving engineers a faster way to explore process conditions without rerunning the full model each time.
Engineers turn analyses into decisions — revising a design, investigating a test failure, adjusting a production process and more.
AI agents can help carry out the steps between those decisions.
Key considerations
- Investor positioning can change fast
- Volatility remains possible near catalysts
- Macro rates and liquidity can dominate flows
Reference reading
- https://blogs.nvidia.com/blog/ai-semiconductor-fabs/#primary
- https://blogs.nvidia.com/blog/author/timothycosta/
- https://blogs.nvidia.com/blog/ai-semiconductor-fabs/#disqus_thread
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Informational only. No financial advice. Do your own research.