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Course contents

  • How to Use This Course
  • Reading Path
  • STA Glossary
  • STA Metrics Reference
  • STA Mastery Cheatsheet
  • Timing Paths and Checks16
  • Constraints & SDC Fundamentals20
  • Delay Graph & Path Analysis16
    • Graph Traversal & Slack
    • Cell vs Net Delay Budgets
    • Path Group Prioritization
      • Path Groups and Weights
      • Path Group Reports
      • Choosing What to Fix First
      • Tracking Closure by Group
    • Report Timing Deep Read
  • Beyond Setup and Hold16
  • Multi-Corner Multi-Mode Signoff16
  • OCV, AOCV, POCV and Variation16
  • Crosstalk and Noise16
  • ECO and Closure16
  • STA Debug Playbook16
  • STA Interview Prep26
  • Capstone Timing Signoff Lab4
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‹Path Group Prioritization
  1. Topics
  2. Static Timing Analysis
  3. Delay Graph & Path Analysis
  4. Path Group Prioritization
  5. Tracking Closure by Group

Part 3 · Delay Graph and Path Analysis · All levels

Tracking Closure by Group

A closure dashboard tracks WNS, TNS and failing endpoints per group over time, with the inputs of each run recorded.

On this page

    Why it matters

    Closure is a trajectory, and a single run tells you almost nothing about it. A per-group history shows whether you are converging, whether a recent change helped or hurt, and when the project can realistically sign off.

    What to record per run

    Field

    Why

    Run identifier and date, netlist and constraint versions

    So the run can be reproduced and compared

    Per group: WNS, TNS, failing endpoints (setup and hold)

    The progress measure

    Corner or view

    Results from different views are not comparable

    Open systemic issues and owners

    What is being worked on and by whom

    Notes on changes since the last run

    Explains movement

    example.py
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    history = [  # (day, group, FEP)
        (1, "clk_core", 5000), (2, "clk_core", 3100), (3, "clk_core", 900), (4, "clk_core", 40), (5, "clk_core", 0),
        (1, "clk_io", 30), (2, "clk_io", 30), (3, "clk_io", 55), (4, "clk_io", 60), (5, "clk_io", 62),
    ]
    def trend(g):
        pts = [f for d, gg, f in history if gg == g]
        return pts[-1] - pts[0]
    assert trend("clk_core") == -5000
    assert trend("clk_io") == 32
    stalled = [g for g in ("clk_core", "clk_io") if trend(g) > 0]
    assert stalled == ["clk_io"]
    print("PASS: converging: clk_core; regressing group:", stalled)

    clk_core converges to zero in five days; clk_io, which looked healthy on day one, slowly regresses from 30 to 62 failing endpoints. The summed total would fall from 5,030 to 62 and hide the regression in one group, and a latest-run view would never show the trend. Tracking per group per day is what catches slow regressions.

    What the review looks for

    • The dashboard shows setup and hold, per group, per view, with a trend line.

    • Every reported improvement is checked against the inputs: a better number from a looser constraint is not progress.

    • Closure criteria (zero failing endpoints, or a signed-off list) are defined at the start.

    Common pitfall

    Quoting percentage clean. "98% of endpoints pass" can hide the 2% that are the hard problem. Use counts and the worst values per group.

    Interview

    Mention per-group trends and the recorded inputs as the way to prove progress rather than assert it.

    Check yourself Why include the constraint version with every run?

    A change in constraints moves slack without any design change. With the version recorded, a jump in results can be explained by the inputs, preventing false progress or false regression.

    Related topics

    • Choosing What to Fix First

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    See also

    • Path Groups and Weights
    • Path Group Reports