UltraSoC collaborates with PDF Solutions to prevent in-life product failures using end-to-end analytics and advanced machine learning techniquesPredictive analytics and proactive maintenance key to reducing costly recalls
April 2nd, 2020 – UltraSoC announced a collaboration with PDF Solutions that combines comprehensive data analytics with advanced machine learning (ML) techniques, with the goal of predicting and preventing chip failures in the field with unprecedented accuracy. The solution will combine in-life information from UltraSoC’s hardware-based behavioral monitors with PDF Solutions’ end-to-end machine learning and analytics platform to identify chips that are likely to fail in the field, allowing OEMs to predict and proactively address issues before they occur.
No other solution can provide such a comprehensive view of historical data from semiconductor manufacturing, test, assembly, supply chain traceability and in-field data within a common semantic data model. This powerful fab-to-field analytics framework is expected to help reduce the impact of product recalls such as those costing the automotive industry $22 billion in 2016, with over 53 million vehicles recalled.
Machine learning solutions rely on the size and quality of the data sets used for training. Over 100 leading semiconductor companies worldwide use the PDF Solutions Exensio® Software Platform to provide this data by collecting semiconductor yield, control, test, and assembly data from more than 21,000 machines worldwide. Leveraging this comprehensive data, engineers use the Exensio Software Platform to monitor, diagnose, and identify manufacturing issues and take immediate action to improve key performance metrics from the factory floor to test operations and assembly.
UltraSoC’s embedded analytics and monitoring technology delivers an essential final piece of the data analytics puzzle to the Exensio platform, with its invaluable data on the in-life digital behavior of the chip or system. UltraSoC monitoring observes functional behavior trends over a period of time to construct a comprehensive picture of potential problems with the device while in use.
Combining in-field monitoring data, manufacturing data, and the appropriate artificial intelligence powered by machine learning, holds the potential to offer chip makers and OEMs a complete predictive analytics platform for their SoCs. The powerful, ML-driven analytics framework can be used to automatically generate alerts, actions and system reports.
Dennis Ciplickas, VP of Advanced Solutions at PDF Solutions, said, “Our Exensio software platform delivers a best-in-class portfolio of advanced data management and analytics for IC manufacturing, characterization and failure analysis in the industry. Connecting to UltraSoC’s in-life monitors and data will enable us to extend our analytics and ML offerings to support a total preventive maintenance solution for semiconductor devices. We are delighted to be collaborating with UltraSoC to enable our mutual customers to attain new levels of insight, achieve better product quality, improve safety, and increase profitability.”
Rupert Baines, CEO of UltraSoC, added: “The value of quality – or conversely, the cost of poor quality – is too high to ignore. We have seen that, with increasing design and manufacturing complexity, plus system sophistication, product failures and recalls also increase. UltraSoC is already applying its intelligent hardware-based monitoring and analytics to a variety of in-life applications, including cybersecurity, functional safety and performance optimization. Working with PDF Solutions allows us to tap into comprehensive manufacturing data and advanced ML technology. The resulting fab-to-field analytics framework will have enormous potential to help manufacturers understand the evolving picture of how their products are behaving in real life, and to predict field failures before they actually happen.”