Technology Computer-Aided Design (TCAD) simulations are essential for semiconductor manufacturing, allowing engineers to virtually design and test devices before physical production, thus saving time and costs. However, these simulations are computationally demanding and time-consuming. AI-augmented TCAD, using tools like NVIDIA's PhysicsNeMo and Apollo, offers a solution by creating fast, deep learning-based surrogate models that significantly reduce simulation times. SK hynix, a leader in memory chip manufacturing, is utilizing these AI frameworks to accelerate the development of high-fidelity models, particularly for processes like etching in semiconductor manufacturing. This approach not only speeds up the design and optimization of semiconductor devices but also allows for more extensive exploration of design possibilities. By leveraging AI physics, TCAD can evolve from providing qualitative guidance to offering a quantitative optimization framework, enhancing research productivity in the semiconductor industry. This matters because it enables faster innovation and development of next-generation semiconductor technologies, crucial for advancing electronics and AI systems.
