The UltraSoC hardware-based monitors will be linked 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.
“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," said Rupert Baines, CEO of UltraSoC. "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.”
Despite the name, the Exensio software has little to do with PDFs. The software is used in by over semiconductor companies for manufacturing, test, assembly, supply chain traceability and in-field data with a common semantic data model. Adding the data from the operation of a chip 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.
The data collected in Exensio is used for the machine learning algorithms that collectand assess the semiconductor yield, control, test, and assembly data from more than 21,000 machines worldwide. Engineers use this data to monitor, diagnose, and identify manufacturing issues. UltraSoC’s embedded analytics and monitoring technology then provides data on the behaviour of the chip or system by monitoring functional behaviour trends over a period of time.
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 system on chip devices