CDC Signoff Dashboard: Worked Example

Worked example

Worked Example for CDC Signoff Dashboard focuses on critical violations trend, closure burn-down, owner SLA adherence. The goal is to convert issue observations into mechanism-backed closure decisions.

A milestone review shows critical violations trend, closure burn-down, owner SLA adherence. Teams disagree on severity. The right move is to isolate one representative issue, prove mechanism class, and decide fix or waiver with explicit residual risk.

Crossing under inspection

CROSSING FLOW — CDC Signoff Dashboard

source clock domain -> launch signal -> crossing structure -> destination sample
      |                    |                 |                    |
   source FF           protocol           sync / fifo         destination FF

Key metric: critical violations trend, closure burn-down, owner SLA adherence

Dashboard dimensions

by block | by severity | by owner | by milestone

Critical trend must decline with evidence quality, not waiver volume.

1. Capture warning, waveform, and owning module context.

2. Tag mode/reset/traffic state for the failure.

3. Validate assumptions against spec and assertions.

4. Compare outcome with weekly CDC dashboard, severity breakdown, closure forecast.

5. Choose one reversible action and define regression upfront.

Did the action work?

BEFORE / AFTER — CDC Signoff Dashboard

open critical issues
  ^
  |  o baseline
  |     o after fix batch
  |         o after protocol proof
  |             o signoff-ready
  +---------------------------------> closure iteration

Track issue burn-down with evidence quality, not only count.

CDC/RDC deep dive

Signoff is an evidence governance problem across teams.

Concept diagram

CDC SIGNOFF LOOP

analysis -> triage -> fix/waive -> review gate -> dashboard

Metric graph

CRITICAL OPEN COUNT

week1 34
week2 22
week3 10
week4 0

Reports and artifacts

• critical by owner

• waiver quality

• review gate status

• closure trend

Mini case study

Pass rates improved while critical waivers aged out of policy; dashboard exposed hidden risk.

Debug branches

• Separate quality from quantity metrics

• enforce waiver expiry

• run gate checklists consistently

Senior review question

Ask: what evidence proves this risk is closed for silicon, not just tool-clean?

Key takeaways

• State crossing class, assumptions, and owner with every issue.

• Run structural and dynamic regressions after each fix.

Common pitfalls

• Treating all warnings as equivalent risk.

• Waiving issues without containment evidence.

• Skipping reset and reconvergence stress after CDC fixes.

Principal CDC/RDC review addendum

Dashboarding translates thousands of crossings into actionable closure metrics by severity, block, owner, and risk state.

Metric: critical violations trend, closure burn-down, owner SLA adherence