Synopsys' PrimeShield Recognized by 2021 World Electronics Achievement Awards

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Synopsys' PrimeShield has received the Product of the Year honor in the EDA/software category from the 2021 World Electronics Achievement Awards.

www.eetasia.com, Nov. 10, 2021 – 

Synopsys Inc.’s PrimeShield design robustness solution has received the Product of the Year honor in the EDA/software category from the 2021 World Electronics Achievement Awards (WEAA).

Part of the Synopsys Fusion Design Platform golden signoff-enabled RTL-to-GDSII design flow, the PrimeShield solution is the industry’s first and most accurate machine learning-driven technology to firmly establish design robustness analysis as a method to minimize failure and maximize power, performance and area (PPA). This is the fourth consecutive year Synopsys has received this WEAA recognition, which honors companies that have made outstanding contributions to the innovation and development of the electronics industry worldwide.

“Synopsys PrimeShield design robustness solution is the first and most accurate of its kind, delivering fast statistical algorithms and unique machine-learning technology to address customers’ increasing variability challenges at smaller geometries,” said Sanjay Bali, vice president of Marketing and Strategy for the Silicon Realization Group at Synopsys. “Through our innovation and leadership, customers developing high-performance CPUs and GPUs, low-power and ultra-low-voltage mobile chips, high-volume designs and automotive SoCs can achieve enhanced reliability, power and performance.”

PrimeShield design robustness solution has been deployed at the Top 5 leading semiconductor companies. Built on the golden timing engine of Synopsys PrimeTime static timing analysis solution, the technology can rapidly identify and drive optimization of bottlenecks at the stage, path and design level that are sensitive to variations such as supply voltage drops or manufacturing variability. The solution delivers up to 10,000x faster results compared to existing methods, with accuracy within 1% of HSPICE full Monte Carlo simulation. It is scalable to volume-production system-on-chips (SoCs) with billions of cells, performing statistical simulation on critical timing paths within minutes versus days or weeks that are required by traditional statistical simulations.

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