A startup called Cognichip said today it has raised $60 million in funding to try and accelerate momentum for the emerging concept of physics-informed chip design powered by advanced artificial intelligence models The round was led by Seligman Ventures and saw participation from Mayfield, Lux Capital, FPV and Candou Ventures, plus Intel Chief Executive Lip-Bu Tan, who will join its board of directors..

Cognichip argues that the semiconductor industry is approaching structural limits as the design of advanced chips becomes more expensive and time-consuming than ever before, requiring years of effort and hundreds of millions of dollars. As a result, the advance of AI itself is slowing, as the chips fail to keep up with the capabilities of more powerful models.

The startup has not built another electronic design automation tool. Instead, it’s aiming to rethink the entire way that chips are designed with its Artificial Chip Intelligence design platform. ACI is a physics-informed foundational model that’s built specifically for chip design. Unlike general-purpose models, it integrates elements such as physical constraints, circuit behavior and the difficulties of manufacturing into the semiconductor design process. This enables it to reason across every step, from the architecture design to verification and even production.

Cognichip argues that traditional chip design processes are too sequential, with engineers moving step-by-step through each workflow. Instead of doing this, the startup’s approach embraces parallelism, which means that multiple design decisions can be explored simultaneously. That’s important because the most advanced chips today span digital, analog and mixed-signal domains, and each part has interdependencies with the other, making optimization extremely difficult. But by embedding physics directly into its foundational model, Cognichip can calculate all of these tradeoffs in ways that data-driven EDA tools cannot.

The startup says this means ACI acts more like an engineering collaborator rather than a design tool, solving problems through advanced reasoning. As a result, Cognichip says it’s able to reduce the effort that goes into chip design by up to 50%.

Cognichip says it’s currently working with more than 30 semiconductor design companies, including some of the industry’s biggest players, and says its platform is now being tested in real-world production workflows. Early adopters report reductions in chip design cycles and costs, along with enhanced performance. The platform also allows chipmakers to maintain their existing manufacturing standards, which is critical for its broader adoption. However, Cognichip declined to name any of its customers, and did not mention any specific chips that its platform has helped to design.

“The semiconductor industry is at a critical juncture; an AI framework for innovation and efficiency will unlock massive global opportunity,” said Tan. “Success in this space requires a rare fusion of deep domain expertise combined with advanced AI research and an end-to-end integrated design approach. Cognichip’s groundbreaking, physics-informed foundation model technology and proven leadership team position it to become a generational company.”

Cognichip’s rise comes at an interesting juncture, where it seems that there’s a growing dependency between AI and the hardware that powers it. Many AI models are reaching performance limits and require more powerful processors, but those chips can take years to design, curtailing progress in the industry. If it succeeds in compressing design timelines into a matter of months instead of years, Cognichip will not only accelerate chip innovation, but potentially boost the momentum of the entire AI ecosystem.

“The next wave of progress to significantly reduce chip design cycles will not come from incremental optimization of existing design tools, but from using AI to parallelize what has historically been a highly serial chip design process,” said Seligman Managing Partner Umesh Padval. “Cognichip is building the foundation for that shift through physics-informed models, curated datasets, and production-ready integration with the semiconductor design stack.”

Image: Cognichip