What "Physics AI" means here
Physics AI is machine learning that is constrained — not just trained — by the governing physical equations of the system it models: Maxwell's equations for electromagnetics, the heat equation for thermal spreading, drift-diffusion for carrier transport, or Fick's law for dopant diffusion. Instead of learning purely from labeled data, the network's loss function includes a physics residual: how badly the network's output violates the PDE it's supposed to satisfy.
Why chip design needs it
Full-physics simulation (finite-element TCAD, SPICE-level electromagnetics, Monte Carlo transport) is accurate but slow — minutes to hours per run. Modern chip design needs thousands of design-space evaluations per day. Physics AI closes that gap:
🌡️ Thermal & Hotspot
A PINN trained on the heat equation predicts junction-temperature maps across a floorplan in milliseconds instead of running a full finite-element thermal solve per candidate layout.
🔌 Electromigration
Neural operators surrogate Black's-equation wire-reliability models across millions of BEOL interconnect segments, flagging failure-prone nets early in place-and-route.
🔬 Process & TCAD
Etch, deposition, and lithography models (see our live simulators) can be accelerated with learned surrogates for rapid process-window exploration, falling back to full physics for final sign-off.
How the training loop differs from ordinary ML
The physics-residual term is computed with automatic differentiation — the same machinery that trains the network also evaluates how well its output satisfies the PDE at arbitrary points, including places with no labeled data at all. That's what lets a physics-informed model generalize into sparsely-sampled regions of the design space.
Where the line is drawn
Physics AI is a speed multiplier for exploration, not a replacement for final-physics sign-off. The practical workflow used across the simulators on this platform:
- Explore — surrogate model screens thousands of candidate process windows or floorplans in the time one full-physics run would take.
- Narrow — the top candidates get re-evaluated with full-fidelity TCAD/FEM/Monte Carlo physics.
- Sign off — tape-out decisions are made on full-physics results, never on the surrogate alone.