Calibre Vision AI: a new era of fast, scalable chip-level DRC debug

By James Paris

As chip designs grow more complex and SoCs reach new heights in size and integration, the challenges of physical verification demand smarter tools and workflows. For teams managing parallel block development, incremental verification and volumes of “dirty” data, legacy methods just can’t keep up. Enter Calibre Vision AI—a major leap forward in debugging methodology for today’s large and complex designs.

Why DRC debug breaks down at scale

Modern physical implementation leverages parallelism: blocks and partitions are created alongside the top-level design, with verification happening at multiple levels simultaneously. While this parallelism boosts efficiency, it also means that checking design rule compliance (DRC)—especially at advanced nodes—can generate millions or even billions of errors. Debug cycles slow to a crawl as engineers wade through seemingly endless lists, using scripts and manual filtering long before they ever get to real root causes.

Traditional debug approaches rely on ASCII output from Calibre nmDRC, which teams review with Calibre RVE (see Figure 1). For small- to mid-size designs, this flow is familiar and practical. But as projects outgrow these solutions, major bottlenecks arise.

Two critical limitations stand out:

• ASCII file constraints: To keep file sizes manageable, default error outputs are capped (often at 1,000 errors per rule). Raising the cap explodes file sizes, causing multi-gigabyte ASCII logs that take minutes just to load. OASIS format, by contrast, holds billions of errors in a file that loads in seconds.
• Slow root cause discovery: ASCII databases offer little context—identifying root patterns and systematic failures is largely a manual effort, compounded by file splitting, lack of hierarchy and missed cross-block issues.

When violations soar into the billions or block completion is delayed, the old approach slows debug to a crawl. Clearly, the industry needs something purpose-built for the new realities of SoC complexity and verification scale.

Calibre Vision AI: a smarter approach for modern debug

Calibre Vision AI is engineered precisely for this new world. It completely redefines the debug paradigm by combining high-speed, instance-complete OASIS results with AI-powered signal grouping. Rather than forcing designers to slog through error lists, Vision AI quickly classifies violations by spatial pattern and context—streamlining the path from error dump to actionable insight.

Why OASIS format changes everything

By exporting results in OASIS, Calibre nmDRC creates a database that is up to fifty times smaller than equivalent ASCII—with all errors, cell relationships and instance hierarchies captured. In one real-world case, an OASIS file just 1.4GB in size contained 3.5 billion errors and loaded in 45 seconds. Traditional ASCII output of the same results would consume 71GB and take over 15 minutes to open.

This efficiency unlocks new capabilities in debug:

• Complete across-the-die error visualization
• Fast error highlighting and navigation
• Block instances analysis of chip-level errors

Teams maintain continuity while accessing truly scalable SoC-level debug—not a compromise between completeness and performance.

AI-driven Signal analysis: from error flood to root cause

The heart of Calibre Vision AI is Signal-based grouping. AI algorithms sift billions of violation markers, automatically clustering checks into one of seven signal types—depending on spatial distribution and pattern. Some signals reflect global failures (“Fails Everywhere”) while others pinpoint local or recurring hotspots (Signature Signals).

This method slashes debug time: what once took days of guesswork now becomes a set of prioritized analysis groups that engineers can visualize, triage and address in minutes. The workflow is intuitive:

• Activate Signals
• Explore densities with heatmaps
• Drill into contextual details (all overlaid on design data)

This AI-guided, instance-aware approach changes the scale—and the experience—of DRC debug.

Integrating debug and design closure

Solving errors in isolation isn’t enough. Calibre Vision AI connects error review directly to action, enabling users to highlight issues in external tools (like place and route), or to share HTML summaries and signal group findings with colleagues. Engineers can flag error locations, assign them for follow-up and maintain full context throughout analysis.

By keeping debug tightly linked to design iteration and team collaboration, Vision AI accelerates closure—not just for one error type or run, but across the complete physical verification cycle.

Results that drive decisions—not delays

Traditional ASCII workflows are finally outpaced. Calibre Vision AI—by leveraging OASIS data, AI-driven grouping and seamless tool integration—brings speed, clarity and focus to DRC debug, no matter how big the dataset. Design teams can now:

• See all errors and their hierarchy, no matter the volume
• Target systemic issues before pouring effort into low-priority lists
• Move between block and full-chip debug modes without switching context or tools
• Share actionable results and summaries quickly, at any point in the flow

These capabilities mean less time fighting your debug platform—and more time delivering reliable, on-schedule silicon.

Ready to dive deeper?

Traditional DRC debug workflows simply aren’t built for the complexity of today’s designs. Calibre Vision AI with OASIS and AI-driven triage changes that—making true chip-level debug fast, intuitive and scalable. To see full technical results, workflow diagrams and Signal grouping explanations, read the complete Calibre Vision AI technical paper.