Using AI Physics for Technology Computer-Aided Design Simulations

Technology Computer-Aided Design (TCAD) simulations, encompassing both process and device simulations, are crucial for modern semiconductor manufacturing. They enable “virtual manufacturing,” allowing engineers to design, build, and test transistors and integrated circuits digitally before committing to the costly physical fabrication process. This approach significantly reduces development time from years to months and saves billions of dollars in experimental manufacturing costs.

These simulations, however, are computationally intensive and can take as long as several weeks to complete, delaying manufacturing deadlines. AI-augmented TCAD is a key solution to address this challenge. That’s where NVIDIA PhysicsNeMo and NVIDIA Apollo come in. The PhysicsNeMo framework lets developers build high-fidelity surrogates using state-of-the-art architectures for engineering and science simulations. Apollo, announced last month at SC25, makes this easier by providing domain specific, pre-trained models.

Engineers at SK hynix, one of the world’s leading memory chip manufacturers, are leveraging AI physics to develop high-fidelity surrogate models to accelerate device and process simulations in the design and manufacturing of semiconductor chips. Using the NVIDIA PhysicsNeMo framework, engineers have fast-tracked the development of proprietary AI models that can unlock tools for significant innovation in device design and manufacturing.

In this blog, we’ll walk you through the steps to get started with PhysicsNeMo to develop your own custom models and share how the TCAD Intelligence team at SK hynix used PhysicsNeMo to accelerate development of its AI physics models.

Tapping into AI physics for TCAD

TCAD is a specialized field of software simulation used to model and optimize the fabrication and physics of semiconductor devices. It’s typically broken into two main parts—process TCAD and device TCAD. Process TCAD simulations model the physical and chemical steps of chip manufacturing, such as deposition, lithography, etching, and ion implantation. Device TCAD simulations, on the other hand, take the final 3D structure predicted by the process simulation and model its electrical behavior. Engineers utilize a variety of simulation solutions for different use cases, ranging from atomic-scale density functional theory (DFT) simulations to chamber-scale computational fluid dynamics (CFD) simulations.

AI-augmented TCAD presents a fundamental disruptive opportunity for semiconductor manufacturers. As transistors shrink to the nanometer scale, the complexity of their behavior increases, making accurate simulations indispensable for designing next-generation devices but making them also orders of magnitude more expensive.

AI surrogate models—which can be created with NVIDIA PhysicsNeMo—are ultra-fast, deep learning-based replicas of slow, physics-based simulations. This approach dramatically accelerates the design and optimization of semiconductor devices by reducing simulation times from hours to milliseconds, enabling engineers to explore a much wider range of possibilities.

PhysicsNeMo provides Python modules to compose scalable and optimized training and inference pipelines to develop and deploy AI surrogates. The PhysicsNeMo framework offers various AI models tuned for science and engineering and enables the combination of physics knowledge with data.

For AI physics researchers and developers exploring the use of neural operators, GNNs, or transformers—or are interested in physics-informed neural networks or a hybrid approach in between—PhysicsNeMo provides an optimized stack that will enable them to train their models at scale. The engineers use the necessary building blocks from PhysicsNeMo to alleviate the need to develop from scratch. This allows them to reduce the effort required to develop detailed AI methodologies and instead focus on using their domain expertise to develop surrogate models for specific physics problems.

Getting started with PhysicsNeMo

The simplest way to get started with PhysicsNeMo for building an AI surrogate is to use one of the reference application recipes. These examples give you a working template for both the training code and the data. Here is the general step-by-step path you would follow, using the official examples as your guide.

Install PhysicsNeMo: First, you need to set up your environment.
1. The easiest way is to use the official NVIDIA NGC container, which has all dependencies (PyTorch, CUDA, etc.) pre-installed. Next, clone the PhysicsNeMo GitHub repository to get the relevant reference application recipes.
2. If you have an existing dev environment setup for PyTorch, you can pip install from source following the steps outlined here.
Let’s assume you are interested in developing a GNN-based surrogate model for TCAD CFD simulations, you would start with the vortex shedding recipe. After replicating the sample, you can start to customize the training pipeline to your own custom data.
You can also evaluate other model architectures like DoMINO or Transolver on your custom data.
The built-in distributed functionality in PhysicsNeMo recipes allows you to scale any of the above architectures to full 3D chip scale simulations.

Let’s take a look at how SK hynix engineers used PhysicsNeMo for one of the many TCAD use cases.

How SK hynix uses AI physics for TCAD

South Korea-based SK hynix is a global leader in producing high-bandwidth memory (HBM), a crucial component in advanced AI accelerators and GPUs. Its products are vital for a wide array of electronics, from data center servers and PCs to smartphones and next-generation AI systems.

The company’s engineers are pioneering the use of AI physics by developing high-fidelity surrogate models to accelerate device and process simulations. Utilizing the NVIDIA PhysicsNeMo framework, they have rapidly advanced their proprietary AI models. An example is the SK hynix TCAD intelligence team’s work on AI surrogate models for etching, an increasingly critical process in semiconductor front-end manufacturing, particularly for advanced memory technologies. By employing predictive modeling to guide the etching process, SK hynix aims to expedite the development of next-generation memory devices.

Figure shows the improvement in accuracy of the AI Physics surrogate model compared to the ground truth TCAD simulation due to the improved methodology. — *Figure 1. The stepwise improvement in accuracy of the surrogate model to predict the etch profile with improvements in the methodology used.*

Accurate prediction of time-varying structures in the etching process is essential for SK hynix. While neural operators are beneficial, they often require large datasets and struggle with data scarcity. To address this, SK hynix adopted the Graph Network-based Simulator (GNS) architectures grounded in Graph Neural Networks (GNNs), which combine numerical time-stepping methods to effectively model geometry changes over time. GNS captures local interactions, representing critical physical properties with minimal training data. However, the existing GNS models were insufficient for effectively emulating the etching process, necessitating the development of additional AI models to enhance the accuracy and efficiency of the emulations.

Methodologies	Improvement
MeshGraphNet(MGN)	Memory requirement decreased
Chamfer Loss used for velocity calculation	Training loss reduced
Re-meshing each Iteration steps	Inference accuracy improved
Feature selection	Inference accuracy improved
Multi-scale message passing	Training loss reduced
Material feature update each iteration steps	Inference accuracy improved

Table 1. AI methodologies employed on the AI surrogate model for etching process

The TCAD Intelligence team at SK hynix believes that AI-augmented TCAD will become a key enabler of research productivity in the semiconductor industry. By leveraging AI-accelerated TCAD predictions, engineers will be able to realistically evaluate tens of thousands of process cases generated from dozens of recipe combinations. This advancement allows TCAD to evolve beyond qualitative guidance and serve as a quantitative optimization framework for semiconductor R&D.

A wide range of AI models that were developed using the PhysicsNeMo framework and GPU-accelerated libraries play a crucial role in enabling these capabilities efficiently.

How to get started with NVIDIA PhysicsNeMo

If you are a TCAD application developer or an AI physics researcher, PhysicsNeMo is a powerful tool in your arsenal to accelerate your AI model development. Instead of building everything from scratch, you can leverage PhysicsNeMo modules and model architectures to build enterprise scale Physics AI solutions with unprecedented speed and simplicity.

TCAD engineers at SK hynix used this approach to focus their domain expertise and efforts on modeling their problems effectively and building skillful models instead of writing training pipelines using low-level libraries.

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