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Monitor Analytics is a silicon data analytics solution that transforms embedded monitor data into explainable, design-aware insights, helping teams:
- Correlate silicon behavior with design intent
- Identify and debug performance and yield risks early during new product introduction (NPI) through high-volume manufacture (HVM) stages of silicon manufacturing
- Gain actionable insights to design and product engineers
By combining design metadata, simulation expectations, and scalable analytics, Monitor Analytics enables a continuous understanding of silicon behavior from first silicon through production.
Why Monitor Analytics?
Modern SoCs are rich with embedded monitors that capture detailed information about power, performance, timing margins, and operating conditions. While this data is invaluable, it is often difficult to interpret, correlate with design intent, and scale across the lifecycle of a product.
- Lifecycle continuity: One analytics concept applied consistently from NPI through HVM
- Design‑aware analytics: Deep links between silicon data, design intent, and simulation
- Flexible deployment: Local analysis for deep dives, web analytics for scalable production insight
- Platform integration: Built as part of the Synopsys Silicon Lifecycle Management (SLM) platform
Monitor Analytics helps engineering teams move from ad‑hoc data inspection to systematic, explainable insight—enabling better decisions at every stage of silicon production.
One analytics concept—applied across the silicon lifecycle
Monitor Analytics is designed to support the different needs of engineering teams as products move from NPI to HVM—using a consistent analytics foundation across both stages.
Monitor Analytics for NPI
During NPI, engineering teams work with limited but critical data from early silicon. The priority is deep analysis, flexibility, and rapid understanding of how silicon compares to design expectations.
Monitor Analytics for NPI enables deep, interactive exploration of early silicon data, optimized for fast iteration and close collaboration with design teams.
- Gap‑to‑target analysis comparing silicon measurements to pre‑silicon models
- Correlation of silicon data with design attributes and simulation intent
- Parametric sensitivity and design‑of‑experiments (DOE) analysis
- Flexible ingest of test data formats used during early bring‑up
- Fast, interactive analysis on engineering workstations or local servers
- Faster root‑cause analysis of performance and timing issues
- Earlier identification of design, model, or methodology gaps
- Knowledge capture that can be reused as products transition to volume
- Reduced risk during ramp by grounding decisions in real silicon behavior
Monitor Analytics for HVM
As products enter high‑volume manufacturing, data volumes grow dramatically and analysis priorities shift. Engineers need scalable analytics, automated insight, and visibility across lots, wafers, and time.
Monitor Analytics for HVM builds on the same foundational concepts used during NPI, while extending them to support production-scale analytics, while still enabling focused, interactive analysis for root-cause investigation.
- Scalable analytics across millions of parts using a web‑based interface
- Automated identification of outliers, trends, and condition‑dependent behavior
- Spatial and temporal analysis to detect process drift and systematic issues
- Trace production behavior back to design assumptions and constraints
- Consistent metrics and navigation from NPI through HVM
- Continuous visibility into silicon health during production
- Faster identification of yield limiters and emerging risks
- Reduced manual effort through guided workflows and automated insights
- Smooth transfer of NPI learnings into volume manufacturing operations