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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
  • Beyond Setup and Hold16
  • Multi-Corner Multi-Mode Signoff16
  • OCV, AOCV, POCV and Variation16
  • Crosstalk and Noise16
    • Delta Delay Mechanism
    • Noise and Glitch Analysis
    • SI ECO Fix Order
    • SI Signoff Reports
      • Reading SI Reports
      • Windows and Filters
      • False Aggressors
      • SI Signoff Checklist
  • ECO and Closure16
  • STA Debug Playbook16
  • STA Interview Prep26
  • Capstone Timing Signoff Lab4
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‹SI Signoff Reports
  1. Topics
  2. Static Timing Analysis
  3. Crosstalk and Noise
  4. SI Signoff Reports
  5. Reading SI Reports

Part 7 · Crosstalk and Noise · All levels

Reading SI Reports

A victim line gives the nominal delay, the aggressors with coupling and window overlap, and the total delta; check the total against the lines.

On this page

    Why it matters

    The worst victims in a crosstalk report decide most of the SI closure work. Reading a victim block carefully, in particular which aggressors count and which were dropped, tells you whether the number can be trusted and where to act.

    An illustrative victim block

    Diagram
    illustrative generic format (not tool output)
    victim: n_data   transition: rise   check: setup   nominal delay: 42.0 ps
    aggressor   coupling (fF)   window overlap   delta (ps)
    a1          3.2             yes              +6.1
    a2          1.8             yes              +3.0
    a3          2.5             no                0.0   (filtered: window)
    total delta: +9.1 ps    delay with SI: 51.1 ps
    example.py
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    nominal = 42.0
    aggressors = [("a1", 3.2, True, 6.1), ("a2", 1.8, True, 3.0), ("a3", 2.5, False, 0.0)]
    total = round(sum(d for _, _, overlap, d in aggressors if overlap), 3)
    assert total == 9.1
    assert round(nominal + total, 3) == 51.1
    counted = [n for n, _, o, _ in aggressors if o]
    filtered = [n for n, _, o, _ in aggressors if not o]
    assert counted == ["a1", "a2"] and filtered == ["a3"]
    coupling_counted = round(sum(c for n, c, o, _ in aggressors if o), 3)
    coupling_total = round(sum(c for _, c, _, _ in aggressors), 3)
    assert (coupling_counted, coupling_total) == (5.0, 7.5)
    per_ff = {n: round(d / c, 2) for n, c, o, d in aggressors if o}
    assert per_ff == {"a1": 1.91, "a2": 1.67}
    print("PASS: total delta", total, "ps; filtered", filtered, "; delta per fF", per_ff)

    The total (+9.1 ps) is the sum of the two counted aggressors (6.1 + 3.0); the third, with 2.5 fF of coupling, is filtered by window and contributes nothing. Two quick checks come from the table: the delta per femtofarad of coupling should be of similar size for similar aggressors (1.91 and 1.67 ps per fF here), and a large coupling with a tiny delta needs an explanation (a filter, a weak edge, an aggressor that is nearly always quiet).

    What the review looks for

    • Largest total deltas first; then the aggressors that cause most of each.

    • A filtered aggressor with large coupling is checked: the filter must be the reason, not an extraction gap.

    • Report columns and units are defined once and used the same way across reports, so deltas can be compared between runs.

    Common pitfall

    Trusting a total without looking at what was filtered. A large filtered coupling can become relevant after an ECO moves the windows.

    Interview

    Say what you check on a victim block: nominal, aggressors, overlap, total, filtered.

    Check yourself How much would the total be if a3's window overlapped and its delta were proportional to coupling at 1.8 ps per fF?

    a3 would add about 2.5 × 1.8 = 4.5 ps, giving a total of about 13.6 ps.

    Related topics

    • Next: Windows and Filters

    • Report Timing Deep Read

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    See also

    • False Aggressors
    • SI Signoff Checklist