Cloud-based Exensio Fabless provides an enterprise-wide, scalable analytics platform
March 2, 2021, Santa Clara, CA – PDF Solutions, Inc. (NASDAQ: PDFS), a leading provider of differentiated data and analytics solutions to the semiconductor and electronics industries, today announced that Melexis, a global supplier of innovative micro-electronic solutions, is utilizing Exensio Fabless for cloud-based semiconductor analytics for test and other product data across multiple sites worldwide.
Over the past five years, Melexis has achieved significant revenue and product growth in its key market segments including automotive, industrial, medical and smart applications. With the number and volume of its products increasing, the size of its product data grew significantly, as did the complexity of the analytics to ensure the continuous improvement of product yield and quality.
Melexis chose to migrate its existing analysis products to the cloud-based Exensio Fabless to provide its engineers with an environment designed to scale to their needs and with higher performance. In addition, the Melexis product engineering team had specific analytic requirements that they wanted to be addressed as part of this move to Exensio Fabless. The flexibility and rapid customization provided by the Exensio Fabless environment was a key factor that enabled PDF Solutions to meet all of Melexis’ engineering needs.
“Exensio Fabless, and specifically the Manufacturing Analytics capability, really helped to break down data barriers internally across Melexis,” said Gino Nys, Quality Improvement Product Manager at Melexis. “PDF Solutions was able to tailor Exensio Fabless with templates that provided the very specific analytics that we needed. It enabled us to find in an efficient way more opportunities to lower the cost-of-yield and cost-of-test.”
“Exensio Fabless was one of the first major analytics platforms that we moved to the cloud inside of Melexis,” said Marc Collignon, Senior IT Business Analyst at Melexis. “We were able to integrate the cloud-based Exensio Fabless with our data sources and our identity platform. From a data management perspective, moving to a cloud infrastructure significantly reduces our effort from an IT perspective.”
“As one of our long term customers of Exensio Fabless, we are very pleased with the success that Melexis has achieved with deployment on the cloud,” said Said Akar, GM and VP of the Exensio Analytics Group at PDF Solutions. “Customers worldwide are adopting Exensio Fabless because it enables them to accelerate new product introduction, manage product yield, quality, and reliability in volume production, and increase visibility into the supply chain with robust data security to safeguard the IP of their ecosystem partners. In addition, our cloud implementation enables lower total cost of ownership, faster rollout of new functionality, and higher analytic performance and capacity, resulting in greater ROI for our customers.”
About PDF Solutions
PDF Solutions (NASDAQ: PDFS) is a leading provider of advanced data analytics, machine learning, and equipment connectivity. PDF Solutions provides a comprehensive end-to-end analytics platform for smart manufacturing connectivity, data collection and management, and machine-learning analytics for the semiconductor and electronics ecosystem, enabling companies improve the yield, quality, and reliability across the entire product lifecycle. Organizations of all sizes around the world rely on the Exensio® and Cimetrix® platforms to connect, collect, monitor, and analyze data at scale to drive efficient and profitable high-volume manufacturing.
Headquartered in Santa Clara, California, PDF Solutions also operates worldwide in Canada, China, France, Germany, Italy, Japan, Korea, and Taiwan. For the Company’s latest news and information, visit https://www.pdf.com
PDF Solutions, Exensio, Cimetrix and the PDF Solutions logo are trademarks or registered trademarks of PDF Solutions, Inc. or its subsidiaries.
Company Contact
David Park
VP of Marketing
(408) 533-8530
david.park@pdf.com