Combinational LEC: Key-Point Matching and Structural Normalization: Expanded Case Study

Extended case study

A formal regression involving Combinational LEC: Key-Point Matching and Structural Normalization reopens late in the release cycle after RTL and constraint updates.

Background

Earlier runs were stable, but model assumptions drifted and property intent was not re-audited after implementation changes.

Symptoms observed

• non-vacuous closure rate, counterexample turnaround time, and requirement-level residual risk trend trends worsen while status dashboards look superficially stable.

• counterexample patterns recur across related properties.

• reviewers disagree on whether failures are real bugs or modeling artifacts.

Investigation timeline

1. Hour 0: freeze RTL, assumptions, and tool settings for reproducibility.

2. Hour 1: classify failures into bug, model mismatch, or weak-property buckets.

3. Hour 2: isolate first divergence and map to requirement intent.

4. Hour 3: apply one constrained change and rerun focused property set.

5. Hour 4: confirm reachability and vacuity quality did not regress.

6. Hour 5: replay representative traces in simulation or equivalent flow.

7. Hour 6: publish closure memo with residual risk classification.

Root cause

Root cause traced to Combinational LEC: Key-Point Matching and Structural Normalization: Combinational LEC proves that two designs produce identical outputs for identical inputs in the same cycle, making it ideal for pure combinational logic transformations and synthesis-preserved cone rewrites.

Fix and validation

• Correct assumption/property scope to preserve legal behavior.

• Add targeted helper checks that expose key intermediate invariants.

• Update runbook and requirement traceability for future regression stability.

Lessons learned

• Status color is not proof quality; audit supporting evidence.

• First-divergence classification outperforms broad trace inspection.

• Constraint and abstraction governance must be versioned and reviewed.

CASE STUDY - Combinational LEC: Key-Point Matching and Structural Normalization
closure slope / vacuity trend / inconclusive aging / replay confidence

Formal deep dive

Equivalence confidence comes from transformation-aware setup and rapid first-divergence diagnosis.

Concept diagram

EQUIVALENCE WORKFLOW

golden and revised design -> mapping and alignment -> mismatch triage -> closure evidence

Metric graph

LEC/SEC DEBUG SIGNALS

setup mismatches        █████
real behavioral deltas  ███
resolved divergences    ███████

Metrics and artifacts to collect

• compare-point match quality

• SEC latency-alignment success

• RTL-to-gate variant coverage

• ECO mismatch root-cause aging

Mini case study

A late ECO mismatch was traced to clock-gating setup, then closed with repeatable SEC alignment rules.

Debug branches

• Classify mismatch source before editing waiver sets.

• Use SEC when latency movement is intentional.

• Replay first divergence in simulation for cross-validation.

Senior review question

Ask: which requirement intent is proven, under which assumptions, and what residual risk remains?

Key takeaways

• Tie each proof claim to assumption boundaries and reachability evidence.

• Prefer minimal reversible fixes and preserve legal behavior visibility.

Common pitfalls

• Treating runtime reduction as proof-quality improvement without audits.

• Declaring closure while critical covers remain unreachable.

• Using broad waivers instead of first-divergence root-cause ownership.

Principal formal review addendum

Combinational LEC: Key-Point Matching and Structural Normalization should be reviewed as a requirement-evidence workflow, not a single status report.

Use non-vacuous closure rate, counterexample turnaround time, and requirement-level residual risk trend as the monitoring lens and formal closure packet: assumptions audit, proof status matrix, counterexample classification, and requirement traceability as closure proof.

Equivalence closure quality depends on transformation-aware setup and first-divergence debug discipline. Strong teams preserve legal reachability while improving convergence.