About Silicon.da

Synopsys Silicon.da production analytics, part of the Silicon Lifecycle Management (SLM) family, spans design through product manufacturing phases. Silicon.da automatically highlights silicon data outliers, enabling engineering teams to quickly identify and correct underlying issues in design and manufacturing. It boosts productivity by consolidating analytics across all manufacturing phases within a single environment while able to process and analyze orders of magnitude more silicon data compared to other solutions.

Key Benefits

productivity

Productivity

Automated data integration, insights and root cause analysis

efficiency

Efficiency

Optimized chip production metrics and silicon operational metrics

scalability

Scalability

Architected to support petabytes of various types of data

Productivity

Most data today is either unused by engineers due to the massive amount or lack of automation on where to look for issues within this data. With Silicon.da, engineers are now able to gain instant value from all data integrated into one solution. Automated analysis and identification of issues or key points of interest is shown in the form of insights. Part-level traceability and debug enables quick root cause analysis along with corrective action back into the supply chain. Sub-die analysis is also available in early NPI product stage to identify systematic issues for failure analysis that are preventing high yield and high-volume production.

Key Features:

  • Actionable insights out-of-the box
  • Automated root cause analysis
  • Accurate Failure Analysis candidate selection

Efficiency

Chip power and performance optimization is made possible by the inclusion of monitors into the design enabling feedback of the monitor data for performing design calibration. An automated recipe flow for quality optimization containing outlier detection techniques is provided during production control back into the manufacturing supply chain. Comprehensive yield optimization is enabled by combining yield trend analysis, diagnostics and failure analysis with improvements made back into the design and/or process. OEE analysis of the tester fleet enables throughput optimization of the chip production. Real-time data collection and production control is provided for ultra fast latency of correcting issues resulting in cost savings and preventing quality escapes.

Key Features:

  • Power and performance optimization
  • Quality, yield and throughput optimization
  • Real-time data collection and production control

Scalability

The enhanced product architecture accommodates petabytes of data across the full breadth of data types including design, monitor, diagnostic, fab and production test which most analytics tools cannot process either this breadth or depth of data. Having a solution able to handle massive amounts of data becomes crucial for performing timely root cause analysis especially when debugging silicon chip issues further downstream in manufacturing or debugging RMAs (return merchandise authorization) or performing historical analysis. Also, having the flexibility to provide users an option for processing and storing their data on the cloud is critical.

Key Features:

  • Petabytes of data
  • Multi domain support
  • Cloud enabled

Features

Silicon.da diagram

With Silicon.da, engineering teams can leverage silicon design, monitor, diagnostic, fab and production test data to improve key chip production metrics such as quality, yield and throughput, as well as key silicon operational metrics such as chip power and performance. Optimizing for both power and performance is made possible with an integrated solution of in-chip monitors and comprehensive analytics. The monitors that are key building blocks of power and performance optimization are: Process, Voltage and Temperature (PVT) and the unique Path Margin Monitor (PMM). Silicon data collected by these monitors are used to improve accuracy of pre-silicon models which can significantly reduce power during the design stage by reducing guard bands and derates.

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