Alsemy’s NVIDIA-powered, physics-informed AI workflow has transformed semiconductor device modeling from a slow, expert-only process into a fast, data-driven engine for R&D and production fabs. Where baseline physics-based models once took years to build and weeks to months of manual parameter fitting per device, Alsemy now delivers complete modeling solutions in just a few weeks of GPU-accelerated training, followed by one-second inference and roughly 10 minutes of customer-specific fine-tuning. This shift compresses time-to-model so dramatically that teams can iterate rapidly on new device architectures, enabling faster DTCO loops and more aggressive technology roadmaps across customers such as SK hynix, LG Display, and the National Nano Fab Center.
Feedback from close collaboration with SK hynix indicates that model accuracy from Alsemy’s solution is comparable to that of a highly experienced process engineer, highlighting three core advantages: reducing R&D turnaround time with automation, avoiding human error that can impact product quality and performance, and enabling further AI-assisted R&D process improvement. At the National Nano Fab Center, R&D researchers and smaller companies can more easily access fab resources by first creating a digital twin of their process, significantly improving their chances of “one-shot” product success. These results are especially meaningful in an environment where, as Dr. Jun-Mo Yang, Principal Research Scientist at National Nano Fab Center, notes, “The importance of manufacturing AI and physical AI is widely recognized, [but] revolutionizing high-tech R&D systems remains extremely challenging” due to legacy processes and limited access to real industrial datasets.
Alsphere is also being adopted as the standard process-prediction model for “BANDI,” the national semiconductor and display data platform NNFC is building with KISTI, opening the door for broader use across publicly funded R&D programs.
For Alsemy’s customers, the impact shows up directly in business results. Process development times and product time‑to‑market shrink, R&D costs drop, and better model accuracy supports higher yield. Because Alsis can be quickly fine‑tuned with customer data and run on CPUs, fabs keep sensitive datasets on‑prem while still benefiting from NVIDIA GPU‑accelerated training. This approach gives Alsemy rare visibility into real manufacturing challenges and, using NVIDIA PhysicsNeMo as a foundation, enables scalable, physics‑informed AI models for semiconductors—setting the stage for a long‑term collaboration that advances both customer roadmaps and the broader PhysicsNeMo ecosystem.