Materials Discovery
AI narrows the search space before synthesis, helping teams qualify candidate materials faster and with less wasted lab work.
Autonomous materials discovery and process modeling
Physical AI for the next era of semiconductor design.
ParaScalr is building physics-based AI tools that connect materials, process, device, and system-level design. The focus is simple: make discovery faster, models sharper, and design decisions more grounded in real physics.
1000x
faster materials calculations
10k-100k
atoms for whole-transistor modeling
Materials to fab
one physics-aware workflow
What we build
One stack, spanning discovery through design.
VirtualFab
Physics-based modeling for process steps, metrology, device behavior, and yield-aware workflows from GDS to compact models.
Physical AI Stack
A phased platform that moves from multi-physics foundation models to materials, device, and 3DIC design workflows.
Why now
Chip design is shifting from die scaling to system coordination.
01
Sub-1nm nodes need physics-aware modeling, not just rule-based automation.
02
Chiplets, 2.5D/3D packaging, and mixed-node systems raise the coordination cost.
03
Design teams need faster feedback loops across materials, process, and device layers.