Vinci’s physics AI foundation model delivers deterministic, solver-accurate warpage analysis across extreme scales—already in production use at leading hardware companies

Vinci today announced the availability of its second core physics capability: thermo-mechanical simulation that predicts “warpage” in hardware designs: how they bend, twist, and deform under real-world thermal conditions. This capability is built on the world’s first foundation model for physics, developed by Vinci to transform hardware design—mirroring how generative AI redefined how humans create, reason, and work with information.

Building on its production-grade thermal platform for simulating thermal behavior in hardware, the new capability allows hardware engineers to predict stress and warpage directly from full-resolution designs, automatically and without manual setup. As with Vinci’s thermal conduction physics, the system delivers consistent, first-principles results using a single pre-trained model that runs securely behind the firewall—without customer tuning or workflow changes.

Across the industry, attention is shifting beyond using AI for digital tasks toward intelligent and predictive systems that can reason about the physical world—materials, heat, stress, reliability, and the constraints of real manufacturing. As hardware designs grow more complex and tightly coupled across scales, physical validation has become the limiting factor in how quickly products can be confidently brought to market.

Vinci’s new thermo-mechanical capability addresses that constraint by making high-fidelity warpage and stress analysis practical at production scale. By enabling deterministic prediction from full-resolution designs—from component-level detail through full system assemblies—teams can identify and minimize risk earlier in the development cycle, when changes are more feasible and outcomes still controllable. Rather than replacing numerical solvers as tools, Vinci operates as a physics intelligence layer—reasoning directly over physical laws and structure, with solver-grade methods serving as external verification.

“Engineering teams don’t need louder claims about AI. They need physics intelligence they can validate, reproduce, and sign off on—at the resolution and throughput modern design cycles demand,” said Hardik Kabaria, Founder and CEO of Vinci. “We’re building an always-on physics layer that makes first-principles simulation a design primitive, not a late-stage checkpoint. Shipping thermo-mechanical warpage extends that foundation, connecting real geometry to material behavior, local stress, and global deformation in a fully automated, deterministic production flow that runs behind the firewall.”

Closing a persistent gap in physical hardware design

Across modern hardware systems, thermo-mechanical reliability analysis increasingly requires reasoning across tightly coupled components and materials—without collapsing real designs into oversimplified blocks or averages. Whether in advanced electronics, energy systems, or other complex assemblies, teams need to understand how local material behavior interacts with real design geometry to assess system-level stress and deformation under real operating conditions.

Vinci closes this gap by operating directly on full-resolution geometry to compute how materials behave and by linking local stress to global warpage in a single, repeatable workflow. Rather than treating components in isolation, the system preserves physical detail across scales and produces deterministic results suitable for production decision-making.

With Vinci, engineers can:

• Analyze thermo-mechanical behavior across full system assemblies, without geometric simplification or reliance on rule-of-mixtures approximations
• Derive effective material behavior directly from real geometry, capturing how layout, stacking, and interfaces impact stress and deformation
• Connect local physics to system-level outcomes deterministically, enabling repeatable warpage and reliability decisions in production

In a representative production benchmark, Vinci completed full thermo-mechanical warpage and stress analysis on extremely large, manufacturing-resolution hardware models—automatically ingesting full-resolution layout files (~1 GB) and executing the entire workflow end-to-end in approximately 30 minutes. These runs included resolving 100 × 100 cm boards with fine-scale features down to ~20 microns, as well as 100 × 100 mm advanced packages with substrate designs containing micron- and submicron-scale features. Across these scenarios, Vinci handled automatic model preparation, meshing, and first-principles convergence at scale, producing stable, repeatable results without manual intervention.

This benchmark demonstrates Vinci’s ability to deliver solver-grade accuracy and determinism at true manufacturing scale—handling real production geometry, extreme model sizes, and fully automated execution–which is not feasible with traditional simulation workflows.

Vinci’s foundation model combines proven physics with an AI-based physics reasoning engine to deliver simulations up to 1,000x faster than traditional tools, with completely automated meshing, without hallucinations. It is pre-trained, runs securely behind customer firewalls, and does not require training on proprietary data, yet produces verified results immediately upon deployment. More than ten semiconductor companies have independently benchmarked Vinci’s results against their existing FEA solvers and experimental data, with Vinci matching or exceeding the accuracy of established methods while delivering results in a fraction of the time, in every case.

Built for production deployment

Vinci’s thermo-mechanical capability runs out of the box using a single, pre-trained physics foundation model shared across all customers—no fine-tuning, custom models, or forward-deployed engineering required. The system is designed for secure production use, running entirely behind the firewall to keep customer IP secure. To learn more about Vinci’s thermo-mechanical capability, visit getvinci.ai.

About Vinci

Vinci is building a foundation model for the physical world—bringing deterministic, solver-accurate reasoning to full-fidelity hardware at manufacturing resolution. The Vinci platform enables engineers to explore, validate, and compare designs continuously, without manual setup, meshing, or per-case tuning. A single pre-trained model generalizes out-of-the-box—without retraining or customer-specific adaptation—across real-world geometries and materials, producing reproducible, production-grade results suitable for automation and sign-off. Vinci deploys securely behind customer firewalls, allowing teams to scale engineering insight while keeping proprietary design data fully in-house.

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