Synopsys has bought Plymouth-based IP designer Moortec for its in-chip monitoring technology specializing in process, voltage and temperature (PVT) sensors. The value of the deal was not disclosed as Synopsys says it is not material to its financial reporting.
The Moortec sensors provide a key component to Synopsys’ new Silicon Lifecycle Management (SLM) platform. This will allow Synopsys to take on Siemens' Mentor business with data from complex chips on leading edge processes. Back in June, Mentor acquired Cambridge-based UltraSoC that also provides in-chip monitoring, but of data rather than physical values.
Data from Moortec's environmental sensors is critical to properly understanding chip performance activity and will enable the SLM platform’s analytics engines to drive more detailed and precise optimizations at each stage of the semiconductor lifecycle, starting with design implementation, and progressing through manufacturing, production test, bring-up and culminating with in-field operation. This is the same strategy as Mentor's Tessent tools.
“We continue to deliver on our roadmap of innovation to provide silicon lifecycle optimization solutions that address the evolving needs of the dynamic semiconductor industry,” said Sassine Ghazi, chief operating officer of Synopsys. “This acquisition accelerates the expansion of our SLM platform by providing our customers with a comprehensive data-analytics-driven solution for devices at the most advanced process nodes.”
In-chip monitoring is now a necessity at advanced process nodes as it enables mission critical management of increasingly variable physical and functional conditions in real-time, thereby increasing performance and reliability. Moortec's in-chip PVT sensors and control subsystems that have been used by hundreds of chip designs on all popular process nodes down to 5nm.
In addition to providing real-time in-chip feedback, data from the Moortec sensors will now be extracted and fed to the platform’s analytics engines. The environmental data provided by these sensors is an essential part of fully understanding complex activities within the chip. Combining this information with data from other