Learn about innovative AI applications in semiconductor device and process simulations through this technical conference talk from SK hynix's Future Technology Research Institute. Explore how AI for Science addresses key challenges in physics-based semiconductor simulations, including computation time reduction and model accuracy improvement. Discover specific implementations of AI technologies like Graph Neural Networks for material screening and Neural Operators for transistor performance optimization. Gain insights into how major tech companies are developing AI-powered physics prediction models, with detailed examples from Microsoft's MatterGen and NVIDIA's advanced Weather Forecast technology. Understand the evolution of Technology Computer-Aided Design (TCAD 3.0) and its integration into semiconductor product development through real-world applications across device, process, equipment, and materials simulation domains.
Overview
Syllabus
Core Die 소자/공정 Simulation의 AI 활용 사례 | SK하이닉스 김정한
Taught by
SK AI SUMMIT 2024