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The complexity of the silicon manufacturing processes has led to an explosion of data. Traditionally, engineering teams have had access to data pertaining to their step in the chip development process, but it¡¯s been more challenging to obtain data from other phases of the chip¡¯s lifecycle. More significantly, the raw data has been difficult to distill into useful insights. There¡¯s a lot to sift through, and engineers need to know what to look for and what to query to make sense of it all. Considering the test data domain alone, there¡¯s data stemming from wafer acceptance testing, bump, wafer sort, assembly, final test, and system-level test. There¡¯s also critical importance in being able to tap into the data throughout the early design and manufacturing process, not just downstream. In short, both the depth and breadth of data support matters to help isolate and solve the root cause of any problems.

With semiconductor content rising in a number of application areas, there is growing urgency to move toward zero-defect approaches. The reality is, semiconductor defects are now commonly measured in parts per billion (ppb) rather than parts per million (ppm). Consider the automotive industry, where safety often hinges on the reliability and high performance of a vehicle¡¯s electronic systems and semiconductor components. Even a seemingly miniscule defect rate can prove costly and potentially harmful and, hence, must be avoided. Never before has there ever been such an importance to accelerate convergence of quality and yield issues leveraging data analytics than now.

Key Core Benefit Areas from Data Analytics Environment

Part of the ²ÝÁñÉçÇø Silicon Lifecycle Management (SLM) Family, Silicon.da processes and analyzes petabytes of silicon data¡ªmore than what most analytics tools are able to handle. With its capacity and intelligence, the solution provides the following key core benefits focused on engineering productivity, silicon efficiency, and tool scalability:

• Enables engineers to easily determine how to improve key chip production metrics (such as yield and quality) and how to enhance key operational metrics (such as chip power and performance)
• Automatically highlights silicon data outliers through its 40+ automated insights, so engineering teams can quickly identify and correct underlying issues in the semiconductor supply chain
• Provides automated root-cause analysis via push-button correlation across design, manufacturing, diagnostics, and test data domains
• Consolidates analytics across all manufacturing phases within a single, sharable environment, preventing time-consuming usage of multiple tools and enabling unified part-level traceability and debug

Silicon.da leverages integration with ²ÝÁñÉçÇø Avalon? CAD navigation and debug solution for performing failure analysis and ²ÝÁñÉçÇø TestMAX? test automation family for a combined end-to-end yield optimization and learning flow. Silicon.da also leverages integration with ²ÝÁñÉçÇø PrimeShield? design robustness analysis and optimization solution together with the ²ÝÁñÉçÇø TestMAX family to provide an industry-first, closed-loop power and performance optimization flow between the post-silicon test chip and the pre-silicon design. Tapping into actual chip monitor data from process, voltage, and temperature (PVT) monitors and path margin monitors enables this high-value power and performance optimization flow. The silicon data gathered by these monitors can help enhance the accuracy of pre-silicon models during the design stage, thus enabling the reduction of unnecessary guard bands and derates to minimize power but still maintain the required performance.

Silicon.da also supports the advanced multi-die systems that are becoming increasingly prevalent for compute-intensive designs like AI and high-performance computing. The solution provides the flexibility to process and/or store data in the cloud.

The Power (and Need) for Unification in Semiconductor Manufacturing

Intelligence and insights gleaned from data provide another tool in the toolbox for engineers to further enhance their chips. At the in-design phase, they can use the analytics to improve power and performance. In ramp, they can aim to reduce yield ramp bring-up time. And in production, they can work to improve chip quality, yield, and throughput.

However, while each lifecycle phase has its own unique set of challenges and solutions, having a solution that can take a holistic approach of unifying the lifecycle phases enables the fastest route to solving the problem as the origin of the problem experienced in one lifecycle phase may have originated in an earlier lifecycle phase. Applying analytics intelligence from design through manufacturing, Silicon.da provides an integrated approach that was previously unavailable in the market. It demonstrates why having an integrated, end-to-end solution is paramount.

In summary, with Silicon.da in their arsenal, engineering teams can turn the explosion of data in silicon design and manufacturing into their competitive advantage.