Diagnosis & Yield Learning: Debug Playbook

Debug playbook

Debug Playbook for Diagnosis & Yield Learning focuses on diagnosis resolution, top failing buckets, yield learning turnaround. The goal is to convert metric movement into mechanism, owner, and release decision.

Debug aims to find the first incorrect assumption, not the loudest downstream symptom. Start with reproducibility and ownership.

Root-cause tree

ROOT-CAUSE TREE - Diagnosis & Yield Learning

diagnosis resolution, top failing buckets, yield learning turnaround regresses
        |
  setup changed?
    /        \
  yes         no
  |            |
constraint    silicon or
or ATPG       physical/test path
 /    \          |
SDC   model    chain/clock/power/diagnosis
diff  diff     isolate first failing signature

1. Freeze run tags for patterns, constraints, and tester setup.

2. Isolate first failing metric bucket and scenario.

3. Classify failure source: model, constraints, physical, or silicon.

4. Prove mechanism with one reduced replay or targeted run.

5. Apply smallest owner-controlled fix.

6. Re-run timing, power, and quality regression matrix.

Review memo template

STAFF DFT REVIEW MEMO - Compression & Diagnosis / Diagnosis & Yield Learning

1. Symptom
   - Watched metric: diagnosis resolution, top failing buckets, yield learning turnaround
   - Failing scenario: <mode/lot/corner/program>
   - Pattern class: <scan/transition/compressed/BIST/JTAG>
   - Tags: <constraints, patterns, tester program, netlist>

2. Mechanism hypothesis
   - Primary mechanism: Fail logs and diagnosis map failing signatures back to likely defect sites, enabling systematic yield learning loops with design and process teams.
   - Competing hypothesis: <constraint issue, model issue, physical issue, silicon issue>
   - Missing evidence: <report, replay, diagnosis trace>

3. Proposed action
   - Minimal reversible change: <constraint fix, architecture tweak, pattern update>
   - Expected metric movement: <delta>
   - Regression risk: timing, power, quality, schedule

4. Signoff
   - Re-run artifact: diagnosis report, fail-log bucket table, yield pareto dashboard
   - Required owners: product test owner, yield engineer, DFT lead
   - Final decision: release, waive, rollback, or escalate

DFT deep dive

Compression saves tester time only when diagnosis observability remains credible.

Concept diagram

COMPRESSION LOOP

EDT/decompressor -> compressed patterns -> compactor responses -> diagnosis

Metric graph

PATTERN vs COVERAGE

coverage up   -> pattern count up
compression up -> pattern count down (until aliasing risk)

Reports and artifacts

• compression ratio dashboard

• pattern count trend

• X-source report

• diagnosis bucket summary

Mini case study

Compactor overflow plus unknown inflation caused false diagnosis; masking policy and channel map corrected.

Debug branches

• Separate X issues from silicon defects

• Replay failing patterns uncompressed

• Track tester memory budget

Senior review question

Ask: what evidence proves this DFT decision is safe for production?

Key takeaways

• State metric, lot/corner context, and pattern tag with every claim.

• Treat timing, power, and quality as one signoff problem.

Common pitfalls

• Chasing coverage without legality checks.

• Ignoring test-power side effects of pattern changes.

• Debugging silicon without reproducible tags.

Principal DFT review addendum

Fail logs and diagnosis map failing signatures back to likely defect sites, enabling systematic yield learning loops with design and process teams.

Metric: diagnosis resolution, top failing buckets, yield learning turnaround