How Polar Performance Materials Increased Yield from 80% to 94% in Two Weeks with Altara

A surge in semiconductor demand quickly outpaced Polar’s plant capacity, forcing R&D trials to compete directly with live production for critical equipment. With every test carrying a significant opportunity cost, Polar needed a way to maximize insights per run, increase yield on existing assets, and uphold the consistency customers expected.

This bottleneck became clear on one production line where yield had stalled at 80%. The path to improvement lay within a complex, interdependent production process. Potential contributing factors included material formulation, press conditions, furnace behavior, sintering profiles, stacking configuration, and interactions between these variables.

Polar had accumulated more than a decade of valuable technical and manufacturing knowledge that could help solve the problem. That knowledge existed across on-premises databases, spreadsheets, lengthy technical reports, SEM images, equipment manuals, CAD files, and the experience of Polar’s scientists and engineers.

The challenge was not a lack of data or expertise. It was making accumulated knowledge accessible, connected, and actionable at manufacturing speed.

This became especially important when problems crossed functional boundaries:

• Product R&D held experimental history and formulation knowledge.
• Process engineering understood equipment behavior and production constraints.
• Quality and product teams connected material performance and process decisions to customer application requirements.

A point solution for one function would leave those cross-functional connections untouched, and the problem was too urgent for a slow, manual approach. Polar needed a secure platform with domain-specific intelligence for advanced materials—one that could connect structured and unstructured information across the full product, process, and application lifecycle and allow its technical teams to reason across all three.

Altara addressed this challenge through three connected capabilities:

1. 01 / Integrate

   Secure data and context integration:

   Altara securely connected to Polar’s existing technical data and knowledge systems. Altara met Polar’s structured and unstructured data where it was without requiring data migrations, so scientists and engineers could perform complex analysis across disparate systems from a single platform.

2. 02 / Connect

   AI knowledge engine:

   Altara’s knowledge engine learned how DOE history, process parameters, equipment constraints, and end-customer requirements were connected. It also captured why earlier trials were run, which variables were held constant, and which ideas had already failed. This gave Altara the context to carefully evaluate yield hypotheses against Polar’s actual process.

3. 03 / Improve

   Agent-powered analysis and continuous improvement:

   Altara’s agent platform used the connected context to generate evidence-backed hypotheses, compare process changes, and pressure-test promising ideas. Each validated result was connected back to the underlying experiments and process history, strengthening future analyses.

Together, Polar and Altara connected product, process, and application knowledge and gave Polar’s technical teams a practical way to reason across all three with greater speed and accuracy.

Christopher, a process engineer at Polar, was tasked with improving yield on a capacity-constrained production line. Previously, determining which experiments to run next could require days or weeks of searching historical data, reviewing past trials, and comparing process conditions. With Altara, he could generate data-backed hypotheses in minutes, pressure-test promising ideas, and validate the strongest ones on the plant floor. He used Altara to analyze chemical binders, powder-mix combinations, sintering ramp and dwell times, and different pellet-stacking configurations in the furnace, ultimately narrowing a broad parameter space to a focused set of high-priority experiments.

Within two weeks, Polar increased yield from 80% to 94% (14-percentage-point increase) without reducing throughput.

“

I’ve always had plenty of ideas on how we could improve yield. But what’s great about Altara is not only can I validate all my ideas faster, but it also generates brand new hypotheses from our data that actually work. It’s like having a team of 10 more engineers across all functions next to me running ideas, simulations, and analyses in parallel.

The improvement had an important business consequence for Polar. Higher yield meant more productive capacity from the equipment already installed, at a time when semiconductor demand was placing increasing pressure on the plant.

Polar’s adoption of Altara went beyond a single project and spanned teams across R&D, quality, product, and process engineering:

1. 01

   R&D scientists analyzed SEM images and automated weekly comparisons of particle-size distributions and statistical model fits, making it easier to compare samples and identify promising experimental candidates.

2. 02

   Process engineers monitored acid-recycling operations, interpreted equipment alarms and interlocks, and distinguished intentional operating changes from process upsets before investigating on the plant floor.

3. 03

   Quality and product teams connected specifications, historical performance, and customer requirements to resolve issues and tailor high-performance materials for specific applications.

Across these workstreams, Altara compressed work that once took hours or days into minutes. Altara has saved Polar more than 1,000 hours and helped prevent significant losses associated with defects and production inefficiencies. Higher yield and fewer defects have increased productive capacity and protected margins, while faster development and application support have helped Polar address customer requirements more quickly.

The value has continued to compound as adoption has grown. New data, expert input, and validated results have become part of Altara’s shared knowledge engine. Each completed analysis strengthens the context available to the next scientist or engineer at Polar. Altara has become the go-to AI platform for technical work across Polar.

“

Altara’s insights helped us improve process consistency and tailor a high-performance product to meet the end customer’s specific application requirements. Now I can efficiently search, gather, clean, and analyze data, transforming complex information into clear and actionable trends. This used to take me days. Nobody has that kind of time. Altara did it in 15 minutes.

For Polar, the value of faster analysis extends beyond manufacturing efficiency.

Higher process consistency and faster access to technical knowledge help Polar respond more quickly to customer requirements, accelerate application development, and maintain tighter process control as material specifications become increasingly demanding.

For customers in semiconductor and other advanced technology markets, this means working with a materials supplier that can more rapidly connect material properties, manufacturing conditions, historical performance, and end-use requirements. In some cases, Polar has cut innovation time to market in half.

The same capabilities that helped Polar improve yield internally also strengthen its ability to support customers developing demanding applications.

“

With Altara as part of the team, our pace of innovation is faster than it’s ever been.

1. 01

   Treat product, process, and application as one interconnected system. The breakthroughs came from connecting historical knowledge, manufacturing constraints, and customer end-use requirements to solve a specific problem.

2. 02

   Make every experiment a shared lesson. Results become more reusable when future teams can see what was tried, why a decision was made, and which constraints shaped the outcome. Altara captures this autonomously.

3. 03

   Use AI to extend engineering expertise, not replace it. Polar’s engineers and scientists provided the materials knowledge, process understanding, and judgment needed to determine which insights were meaningful. Altara allowed them to apply that expertise across a much larger body of information and evaluate ideas much faster.

4. 04

   Measure the impact beyond hours saved. Faster analysis for complex scientific and engineering problems is only step one. Focus on measurable success through higher yield, more consistent quality, faster R&D cycles, and better products that meet end-customer requirements.

Polar’s experience demonstrates what becomes possible when deep materials expertise, manufacturing knowledge, and modern AI capabilities come together to address real production challenges and customer growth opportunities.