Vinci Emerges from Stealth to Transform Semiconductor Design and Simulation
Shannon Davis
6 days ago
Vinci, the pioneer of Physics-Driven AI for hardware design and simulation, emerged from stealth today and announced $46M in Seed and Series A funding led by Xora Innovation, with participation from Khosla Ventures and Eclipse. The company has unveiled a physics-driven AI system that operates like a team of hardware engineers, running thousands of verified simulations in hours, not weeks.
Vinci was founded by Hardik Kabaria, a leading expert in computational geometry whose doctoral work at Stanford helped solve one of the hardest problems in simulation — automating high-fidelity meshing for complex, real-world geometries — and by Sarah Osentoski, a pioneer in large-scale machine learning and autonomous systems. Together, they unite two rarely connected domains: deep, physics-based simulation and production-grade AI. Their expertise has drawn an exceptional engineering team, uniting top industry talent with some of the field’s leading researchers. The result is a physics-driven AI platform that pairs the accuracy engineers rely on with the scale and automation the next decade of hardware design requires.
Accelerating Workflows and Delivering Accuracy
Rising system complexity in areas such as advanced chip packaging, and 2.5D/3D IC is pushing traditional FEA-based simulation tools beyond their limits in speed, resolution and accuracy. Traditional simulation workflows are time-intensive, break down on full manufacturing-resolution geometry and rely on narrow domain expertise that is rapidly becoming a talent bottleneck. Following more than two years in stealth development, Vinci today announced the public debut of its physics-driven AI system, to solve this critical gap.
“At Vinci our goal is to let any engineer see how their design will perform once built,” said Hardik Kabaria, Founder and CEO of Vinci. “Vinci empowers engineers to simulate how designs will perform in seconds instead of days, doing so at a fraction of the compute cost. On next-generation geometries that conventional tools must simplify, such as nanometer-scale components on centimeter-scale dies, Vinci maintains full-fidelity accuracy.”
Vinci’s agentic system combines proven physics methods with an AI model to deliver 1,000× faster simulations, without meshing, without hallucinations and with guaranteed accuracy*. While many AI solutions in this space remain aspirational, Vinci’s system is already deployed, powering next-generation design programs at three leading semiconductor manufacturers. Pre-trained and production-ready, the system operates securely behind customer firewalls, requires no training on proprietary data and delivers verified results immediately upon deployment.
On top of these deployments, more than ten semiconductor companies have also independently benchmarked Vinci’s results against their traditional FEA solvers and experimental data. In every case, Vinci’s simulations matched or exceeded the accuracy of established methods and in several instances, correlated closely with experimental data — all while delivering results in a fraction of the time.
“Few teams combine deep physics expertise with the ability to ship real, production-ready software,” said Charly Mwangi, Partner at Eclipse. “Vinci’s technology is already demonstrating value in the field — accelerating workflows and delivering accuracy that engineers can trust.”
Vinci’s Series A round is led by Xora Innovation, with participation from Khosla Ventures and Eclipse. In total, Vinci has raised $46M to date.
“Vinci has demonstrated the ability to deliver lightning-fast, high-accuracy simulations without requiring customer data for some of the world’s most complex physical devices, state-of-the-art semiconductor packages.” said Phil Inagaki, Managing Partner & Chief Investment Officer at Xora. “Soon, we believe that Vinci’s platform will deliver not only simulation, but also co-design capabilities across a broad range of physics and hardware products, which will result in a radical expansion of what has been traditionally viewed as the EDA market.”
*Copilot:
Here’s the explanation of that text, grounded in the surrounding page content you’re viewing:
Breaking Down the Statement “Vinci’s agentic system combines proven physics methods with an AI model to deliver 1,000× faster simulations, without meshing, without hallucinations and with guaranteed accuracy.”
- Agentic system: Vinci has built an AI platform that acts like a team of engineers, autonomously running thousands of simulations. It’s “agentic” because it doesn’t just passively compute—it actively manages workflows, decisions, and simulation tasks.
- Proven physics methods + AI model: Unlike purely data-driven AI, Vinci integrates established physics-based techniques with machine learning. This ensures that results are grounded in real-world physics, not just statistical guesses.
- 1,000× faster simulations: Traditional finite element analysis (FEA) tools can take days or weeks to run high-fidelity simulations. Vinci’s system accelerates this process dramatically, producing results in hours or even seconds.
- Without meshing: Meshing—the process of breaking down complex geometries into smaller elements for simulation—is one of the most time-consuming steps in traditional workflows. Vinci’s system eliminates this step, thanks to Hardik Kabaria’s expertise in computational geometry and automated high-fidelity meshing.
- Without hallucinations: Many AI systems risk producing inaccurate or fabricated results. Vinci emphasizes that its platform avoids this by sticking to verified physics principles and delivering reproducible, trustworthy outputs.
- Guaranteed accuracy: The company highlights that its simulations consistently match or exceed the accuracy of traditional FEA solvers and even experimental data, giving engineers confidence in the results.
Context from the Page- Vinci emerged from stealth with $46M in funding and is already deployed at three leading semiconductor manufacturers.
- It addresses bottlenecks in advanced chip packaging and 2.5D/3D IC design, where conventional tools struggle with speed and resolution.
- The system is pre-trained, production-ready, and operates securely behind customer firewalls—requiring no proprietary data to function
Why It Matters
This statement captures Vinci’s core innovation: merging physics and AI to overcome the limitations of traditional simulation tools. By removing meshing, avoiding hallucinations, and ensuring accuracy, Vinci is positioning itself as a transformative force in semiconductor design—making workflows faster, more scalable, and more reliable.
Would you like me to also compare Vinci’s approach to traditional FEA tools in more detail, so you can see exactly what makes their “agentic system” different? |
