Chipmakers are finding it nearly impossible to analyze and detect quality, reliability, and yield issues with traditional tools and methods thanks to the increasing complexity in manufacturing and testing. To address these issues, Synopsys, Inc. has developed a data-analytics-driven approach to optimizing SoCs from the design phase through to end-user deployment with the launch of its Silicon Lifecycle Management (SLM) platform.
The SLM platform, coupled with Synopsys’ Fusion Design Platform, is designed to provide visibility into critical performance, reliability and security issues for the entire lifespan of a chip. This translates into new insights for both SoC teams and their customers to help optimize operational activities at each stage of the device and system lifecycles.
“Addressing critical chip performance and reliability issues is a multi-billion dollar issue that doesn’t stop at tape out,” said Richard Wawrzyniak, principal market analyst for ASIC & SoC, Semico Research Corp., in a statement. “It requires a new way of looking at the entirety of how an IC is designed, built and used. Providing access to device data throughout the entire chip life span, and enabling on-going ‘in life’ feedback and optimization through specialized analytics will allow a more efficient and effective way to address the semiconductor-related quality and security challenges system companies face in all industries.”
The driver behind the new platform is the increased difficulty in achieving quality and reliability in silicon due to the growing complexity of electronics systems, along with a lower tolerance for performance degradation and the need to meet functional safety and security requirements. As a result, a new approach was “needed to address how silicon-based systems are developed, operated, and maintained,” said Synopsis.
One example cited is the cost savings in key applications such as data centers and networking where performance and power improvements can be in the billions of dollars. Synopsis believes these savings can be achieved by managing each phase of a chip’s and system’s lifecycle from development to deployment.
The company launched the Synopsys SLM platform with a full roadmap over the next two years, based on two principles. “Gather as much useful data about each chip as possible and analyze that data throughout its entire lifecycle to gain actionable insights into improving chip and system related activities.”
The first principle expands on the data already available from test and product engineering with deep visibility into each chip’s operation through monitors and sensors that are embedded throughout each chip and measures targeted activities across a wide set of contexts and conditions, said Synopsis, while the second principle applies targeted analytics engines that operate on available chip data to enable optimizations at each stage of the semiconductor lifecycle, from design implementation to in-field operation.
The targeted analytics engines that operate on available chip data to enable optimizations include PrimeShield, SiliconDash and Yield Explorer, and the Synopsys SLM Adaptive Learning Engine and Synopsys SLM Embedded Learning Engine.
So what do these analytics tools provide? PrimeShield leverages silicon data-based timing model calibration to minimize required margins and advanced analytics to optimize design PPA, reliability, and silicon predictability. SiliconDash is a semiconductor manufacturing analytics engine and Yield Explorer is a design yield analysis engine that uses fab and test data enhanced with monitor and sensor data to optimize manufacturing and test operational efficiencies and to improve yield. The SLM learning engines offers self-analysis as well as safety, security, and predictive maintenance capabilities.
During a recent presentation at the company’s Digital Design Technology Symposium, Pascal Sotiaux, engineering leader and test support manager, at STMicroelectronics, described how ST uses SiliconDash to help increase engineering and operations efficiency to make informed decisions and to improve operational KPIs.
With the amount of manufacturing and test data together with the increasing complexity of manufacturing and test processes and product sophistication, it’s virtually impossible to analyze and detect all quality and yield issues with traditional tools and methods, he said.
For ST, mass production and big data translates into 1 billion test results per week that need to be analyzed. “With traditional tools, it’s impossible,” said Sotiaux. With the [SiliconDash] tool, ST has achieved a 10× improvement in efficiency, compared to the past, he said.
He calls SiliconDash a “Swiss knife of data analysis” where you can find a view, recipe, or report to analyze your production in real time. It’s a complete data analysis tool that can help speed up the analysis with a large view of production, he said.
The tool is capable, on one click, to give the main information in a summary sheet with all the links and make a deeper analysis from the trend up to the die with traceability, Sotiaux explained. It also is capable of providing in real time the yield loss or gain with the interactive mode to improve my production, he added.
Advertisement
Learn more about Synopsis
