Physical-Aware Debug: Review Checklist

Review checklist

Review Checklist for Physical-Aware Debug focuses on logic-vs-physical delta isolation time, root-cause class distribution. The goal is to connect observed QoR movement to mechanism, ownership, and regression risk.

• Run manifest is attached (RTL/SDC/libs/switches).

• Primary metric and impacted lane are named.

• Mechanism hypothesis is evidence-backed.

• Fix is reversible and owner-assigned.

• Regression matrix includes timing, area, power, and equivalence as needed.

• Owners signed: synthesis owner, PD owner, STA owner.

Sign-off ownership

OWNERSHIP MAP — Physical-Aware Debug

artifact              owner
----------------      -----------------
compile owner       synthesis owner
timing/power owner  PD owner
cross-team review   STA owner

Every QoR movement needs a named owner before ECO.

Synthesis deep dive

Physical-aware synthesis reduces logic-to-route surprise when correlation is monitored.

Concept diagram

PHYSICAL-AWARE LOOP

topo compile -> congestion hints -> handoff -> PD correlation

Metric graph

CORRELATION DELTA

logic view WNS      ███████
topo view WNS       █████
route trial WNS     ████

Reports and artifacts

• topo QoR

• congestion heatmap

• handoff manifest

• delta compare vs PD

Mini case study

Topo compile looked clean, but hotspot congestion predicted route failure and prevented late ECO churn.

Debug branches

• Check hotspot ownership

• Bound correlation drift

• Escalate if handoff assumptions stale

Senior review question

Ask: what evidence proves this QoR move is real and stable?

Key takeaways

• State exact run context (RTL, SDC, libs, switches) with every QoR claim.

• Re-run timing, area, and power regressions after each synthesis ECO.

Common pitfalls

• Comparing runs with mismatched constraints or library views.

• Timing-only fixes that violate power or area budgets.

• Skipping equivalence checks after structural changes.

Principal synthesis review addendum

Physical-aware debug classifies QoR regressions into modeling error, placement pressure, or true logic issues before ECO churn starts.

Metric: logic-vs-physical delta isolation time, root-cause class distribution