AI Computing &System Architecture
Physics-Informed AI Digital Twin for Ion Implantation
PINN prediction replaces offline exploration, enabling <10 second simulation and interactive ion trajectory visualization.
Integrates PINN ion implantation models to rapidly predict 3D ion motion. Completes simulations in <10 seconds, boosts existing workflow efficiency by >30%, and supports interactive ion visualization.
Integrates PINN in implantation models trained with NVIDIA PhysicsNeMo™ to rapidly predict 3D ion motion.
Completes simulations in <10 seconds, boosts existing workflow efficiency by >30%, and supports interactive ion visualization powered by NVIDIA Omniverse™ Kit.
By integrating Physics-Informed Neural Networks (PINN) trained with NVIDIA PhysicsNeMo™ and 3D digital twin technology powered by NVIDIA Omniverse™ Kit, a visualization simulation and analysis platform for semiconductor ion implantation is built. Expected benefits include real-time presentation of 3D ion implantation distribution and depth, provision of intuitive visualization and interpretation tools, realistic simulation of ion diffusion and process evolution within the crystal lattice, and assistance in optimizing process observation and parameter adjustment. The primary execution phases are as follows:
1. AI Model Development and Training
2. 3D Digital Twin Scene Construction
3. Ion Distribution Visualization and Dynamic Simulation
4. System Integration and Platform Deployment
