The intelligence infrastructure for hardware engineering
Know how it will behave before you build it. Ask the design. See the physics. Make the next decision.
Preparing the living mark
The category
Physics that answers while the design can still change. Solver-accurate, deterministic, at manufacturing resolution. Asked in plain language, answered on the object.
An animated product demonstration. Choose a physics above or a study under Recents to change the content. Thermal baseline and sweep values are from Vinci’s demonstration. Transient and warpage treatments are illustrative; rendered fields are not product output. Other workspace controls are illustrative.
Benchmarked, not asserted. Solver-accurate against commercial FEA and experiment; deterministic: identical inputs, identical outputs. The methodology page will demonstrate it.
See the resultsThe problem
Setup takes hours and compute takes days, so the physics gets simplified to fit. Every shortcut widens the gap between design intent and as-built reality, and by the time the answer comes back the cost of change has compounded.
The shift
Continuous Physics Reasoning replaces one simulation at the end with deterministic, solver-accurate answers throughout the design process, on native design data, at manufacturing resolution. No human meshing, no manual setup. The model does the work. The engineer directs it and decides what the results mean.
Illustrative workflow comparison, not measured timing.
One system
One Foundation Model for Physics. The physics below ship today; the rest are in Vinci’s stated order.
“Will this stay under target with the heat sink we specified?”
Peak, distribution and margin on the full package, read from the object.
Try it above“How does the hotspot form after power-on?”
The field stepped in time, so you see where heat forms and in what order.
Try it above“Where does this stack warp as it cools?”
Stress and warpage on the same object, from the same native design data.
Try it above“What does the airflow actually do?”
Thermal convection is the next physics on the roadmap.
“Where does the field couple into the package?”
Electromagnetics follows convection.
“What happens when the physics interact?”
Cross-domain reasoning, after single-physics coverage is complete.
Who it is for
“Will this ship without a late surprise?”
Answers arrive while decisions are still cheap. Physics moves left, before the cost of change compounds.
“Can I trust it for signoff?”
Deterministic and solver-accurate, benchmarked against commercial FEA and experiment. The same input gives the same answer, every time.
“Can I ask without building a model first?”
Native design data in, no human meshing, no manual setup. Ask in plain language and read the answer on the object.
“What does this change?”
Physics becomes infrastructure: always on, from chip to package to board to system. Semiconductors are the proof, not the limit.
Numbers we stand behind
Reference figure single node
219seconds
A 1.2-billion-degree-of-freedom model at 3-micron resolution, solved in about 219 seconds on a single node. No human meshing, no manual setup.
Design of experiments thermal · customer-reported
200 cases in about an hour
Setup under a minute. The legacy workflow took roughly 16 hours.
Adoption consecutive 30-day periods
4,800 → 25,600
Simulations run by one semiconductor team. Adoption is the story.
Speed general claim
Up to 1000×
What matters is when the physics arrives: while the design can still change.
Trust training
No customer data
The model learns physics from first principles, never from your designs.
“Vinci reduces full-package runtime from hours to seconds, without losing accuracy.”
How it works
Vinci is built around a Foundation Model for Physics that learns deterministic physical behavior from first principles rather than from human-generated text or images. The platform combines a pretrained neural foundation model with GPU-native numerical physics kernels and an automated engineering workflow that operates directly on native design data.
Native design files in, at full fidelity. GDS, OASIS, STEP, IPC-2581. Nothing is simplified to fit.
The Foundation Model predicts the field from first principles. That prediction is the starting point, not the answer.
GPU-native physics kernels refine the prediction to solver accuracy, at manufacturing resolution.
The model learns physics itself. Not text, not images, and never customer designs.
The physical stack
Already deployed on production engineering programs at leading semiconductor companies. Heat does not stop at the chip, and neither does Vinci.
Where it goes next Ambition
Everything downstream of the chip: the robots, vehicles, data centers, aircraft and autonomous systems those chips go into. Anything in the world with a chip in it.
Watch it work
Eighteen layers, one question, peak and margin read from the object.
Play above →Watch the field re-solve as the top-surface convection changes. Fails at 125, passes at 144.
Play above →Peer-reviewed, benchmarked against commercial solvers.
Read the study ↗Bring a native design file. Leave with the physics.
