• Collaboration to combine radiography, computed tomography and ultrasound capabilities to develop a full-lifecycle inspection solution for battery manufacturing, development and field service ​
• Joint inspection solution to be first in the market that can detect failure modes across the production lifecycle, providing battery manufacturers with cost efficiencies and higher yield productivity

Huerth, Germany and Emeryville, CA – March 31, 2026 – Waygate Technologies, a Baker Hughes business and global leader in non-destructive testing (NDT) solutions for industrial and energy infrastructure inspection, and Liminal Insights, a pioneer in AI-driven ultrasound inspection for batteries, today announced a strategic technology and channel collaboration to deliver the industry’s first integrated multi-modal inspection solution for battery manufacturing.

Under the terms of the agreement, the two companies will collaborate across battery manufacturing, technology development, and services inspection, including the development of next-generation probes and integrated service offerings. In addition, Waygate Technologies will be Liminal’s preferred channel and integration partner to battery gigafactory and other battery field customers, utilizing Liminal’s EchoStat inspection system across in-line inspections for high-volume battery production operations.

The goal of the joint effort is to deepen collaboration across manufacturing, technology development, and services. Both companies will explore joint product development, co-engineering of next-generation probes, and integrated service offerings for a fundamental inspection challenge that battery factories worldwide are facing: up to 90% of EV battery cells contain some form of anomaly, yet no single inspection technology can detect all failure modes across the production lifecycle.  
 

Collaboration to drive higher yield and cost efficiencies across the battery lifecycle

By bringing together the industrial computed tomography (CT) and radiography leadership of Waygate Technologies with Liminal’s real-time ultrasound AI analytics, the two companies will be able to offer a full-lifecycle inspection capability spanning cell production through to module assembly and field service.

For radiography and CT, the collaboration leverages CT systems from Waygate Technologies for quality assurance and failure analysis in laboratory environments, R&D-grade nano CT for cell development and materials characterization, and high energy CT for battery pack and module level inspection. The Waygate Technologies Krautkrämer product line adds ultrasound solutions from phased array electronics to advanced beam-forming algorithms and probes designed and purpose-built in house for industrial environments. Co-development with Liminal will combine the company’s proprietary array probe technology and signal processing electronics with Liminal’s EchoStat hardware platforms and AI analytics layer, creating a next-generation inline ultrasonic testing (UT) platform with superior sensitivity and production-line robustness.

Through the combination of multiple NDT modalities – X-ray CT as ground truth, ultrasound for inline screening, and machine learning to close the feedback loop with process data – manufacturers will gain earlier defect intervention, reduced end-of-line scrap, and data-driven process optimization.

“Even a fraction of a percent yield improvement at gigafactory scale translates to tens of millions in annual savings,” says Paul Perera, Director of Strategy, Technology & Partnerships at Waygate Technologies, a Baker Hughes business. “The goal of combining the unique strengths of both companies is to significantly increase productivity for our customers.”

“Multi-modality is the future of battery inspection. Waygate Technologies’ global installed base and deep NDT heritage make them the ideal partner to scale our EchoStat in-line inspection systems,” adds Shaurjo Biswas, CEO, Liminal Insights. “Together, we offer battery manufacturers a single-source inspection solution from R&D through volume production that no competitor can match today.”
 

Proven Results at Battery R&D Center in the UK

Waygate Technologies maintains a presence at the UK Battery Industrialisation Centre (UKBIC) in Coventry, UK – one of Europe’s leading battery process development facilities. A joint proof-of-concept program has tested over 400 battery cells through the combined UT and CT workflow, building a comprehensive battery data science dataset. The program has demonstrated that the multi-modal approach delivers over 10% cost savings versus single-modality inspection, with a further 12% improvement in defect capture rate.

The announcement of this strategic collaboration coincides with the commissioning of UKBIC’s new FIL line, providing an ideal environment to validate the integrated Waygate Technologies and Liminal inspection workflow at production-representative scale – from electrolyte filling through formation and aging – before deployment to commercial gigafactories.

Learn more and about the industrial inspection portfolio from Waygate Technologies and the partnership with Liminal:

• Waygate Technologies NDT for Batteries
• Waygate Technologies NDT for Battery and Automotive Manufacturers
• Waygate Technologies LinkedIn Channel
• Waygate Technologies YouTube Channel

The joint inspection solution is set to be the first on the market capable of detecting failure modes across the production lifecycle

Waygate Technologies’ Phoenix V|tome|x M Neo: The world‘s most flexible industrial dual-tube micro/nano CT scanner

Liminal’s EchoStat ARRAY: Ultrasonic inspection system for inline testing

Source: Baker Hughes

Normal Computing has raised $50 million in a round led by Samsung Catalyst as the startup pursues a two-pronged bet on the future of AI hardware: using AI to help semiconductor companies design chips more efficiently, while also developing a new kind of processor aimed at reducing energy use.

New investors include Galvanize, Brevan Howard Macro Venture Fund, and ArcTern Ventures, alongside existing backers Celesta Capital, Drive Capital, Eric Schmidt’s First Spark Ventures, and Micron Ventures.

CEO Faris Sbahi told Fortune the company’s software platform is already being used by more than half of the top 10 semiconductor companies by revenue, as it targets one of the industry’s biggest challenges: the rising cost and complexity of designing advanced AI chips, where even small errors can lead to expensive delays and rework.

Designing advanced AI chips has become so complex that even getting a design to “tape-out”—the point where it’s finalized for manufacturing—is increasingly prone to costly failure. Modern AI chips, which pack in tens of billions of transistors to support today’s frontier models, can cost more than $500 million to develop before a single unit ships.

Normal, founded in 2022 by former engineers and scientists from Google Brain, Google X, and Palantir, is also using its chip design software internally to build its own experimental AI hardware. It has already taped out a prototype chip using the company’s “thermodynamic” approach, which uses the inherent randomness of physical systems to compute more efficiently than traditional GPUs. It’s an early step in a longer-term effort to significantly reduce the energy demands of AI.

“The mission of the company is to go after this so-called AI energy crisis,” said Sbahi. “Data centers are expected to hit an energy wall around 2030, and most of the strategy now is to find new ways to acquire more energy—but our position is to solve the problem in terms of the hardware that we’re using.”

Seeking alternatives to existing AI hardware

Normal Computing is part of a growing group of startups exploring alternatives to conventional AI hardware, including Unconventional AI, led by former Intel AI chief Naveen Rao, which raised a $475 million seed round in January led by Andreessen Horowitz and Lightspeed Ventures. Another is Extropic, which is developing probabilistic AI chips based on a different technical approach.

Sbahi said the company chose the name “Normal Computing” to reflect its view that its approach is closer to how computation should naturally work. “We think this is the more normal way of computing,” he said, pointing to how the company’s software and hardware are designed to align with the underlying physics. “The software really matches the hardware.”

While building energy-efficient AI chips is the company’s long-term goal—initially focused on inference workloads for generative AI—the current fundraise will focus on scaling Normal’s commercial software business.

“Hopefully someday we’ll be integrated into mainstream semiconductor design manufacturing,” said Sbahi. He added that the semiconductor industry’s high costs and complexity make it difficult for new approaches to break in, which is why Normal has focused on working with existing chipmakers rather than trying to disrupt the system from the outside.

“It’s very expensive to make mistakes,” he said.

Source: Fortune