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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. Path Group Reports

Part 3 · Delay Graph and Path Analysis · All levels

Path Group Reports

A per-group summary lists WNS, TNS and failing counts; reading it correctly separates systemic problems from isolated ones.

On this page

    Why it matters

    The summary is the first thing read in every closure meeting. It is only useful if you can turn its numbers into hypotheses: a high endpoint count suggests a shared cause, a deep WNS with a low count suggests a few hard paths, a growing TNS with constant WNS suggests creeping degradation.

    Reading patterns

    Pattern in the summary

    Likely meaning

    First check

    Many failing endpoints, small WNS

    Systemic: clock uncertainty, constraint, derate

    Constraints and clock model

    Few failing endpoints, large WNS

    A handful of hard paths

    Each path's breakdown

    WNS unchanged, TNS shrinking

    Progress on many paths, worst still open

    Is the worst path structurally limited?

    WNS improving, TNS growing

    Fix moved violations around

    What changed in the other groups

    Group suddenly empty

    A clock or constraint was lost

    Clock audit

    example.py
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    run_a = {"clk_core": (-0.05, -0.20, 6), "clk_io": (-0.40, -0.67, 3)}
    run_b = {"clk_core": (-0.05, -0.05, 1), "clk_io": (-0.25, -0.80, 7)}
    def delta(a, b): return tuple(round(y - x, 2) for x, y in zip(a, b))
    changes = {g: delta(run_a[g], run_b[g]) for g in run_a}
    assert changes == {"clk_core": (0.0, 0.15, -5), "clk_io": (0.15, -0.13, 4)}
    # clk_io: WNS better by 0.15 but TNS worse by 0.13 and 4 more failing endpoints: moved violations around
    print("PASS: run-to-run change (WNS, TNS, FEP):", changes)

    Between the two runs clk_core improved (five fewer failing endpoints, TNS better by 0.15) while clk_io's WNS improved by 0.15 ns but its TNS worsened and four more endpoints fail: a fix helped the worst path and damaged others. Looking at WNS alone would call this progress.

    What the review looks for

    • Run-to-run comparisons show all three numbers per group, and the comparison states the inputs that changed.

    • A group whose WNS improves but whose count or TNS worsens triggers a look at what the fix displaced.

    • Hold and setup summaries are reviewed side by side; a setup fix must not worsen hold.

    Common pitfall

    Comparing summaries from runs with different constraints or parasitics. A difference in inputs looks like a result change. Record inputs with each summary.

    Interview

    Use the pattern table: many endpoints and small WNS means look for a shared cause; few endpoints and large WNS means look at each path.

    Check yourself WNS stays at -0.12 ns but TNS goes from -45 ns to -12 ns. What happened?

    Many paths were fixed; the worst path remains. That is real progress even though WNS did not move, so a TNS and count view is needed alongside WNS.

    Related topics

    • Path Groups and Weights

    • Next: Choosing What to Fix First

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    See also

    • Tracking Closure by Group