Diagnosis & Yield Learning: Review Checklist

Review checklist

Review Checklist 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.

• Run tags and scenario context are explicit.

• Constraints and legality assumptions are documented.

• Quality, timing, and test-power checks are included.

• Diagnosis evidence supports residual-risk decision.

• Owners signed: product test owner, yield engineer, DFT lead.

Signoff ownership

OWNERSHIP MAP - Diagnosis & Yield Learning

artifact              owner
----------------      -----------------
architecture/report product test owner
constraints/setup   yield engineer
physical/test       DFT lead

Name an owner for each failing metric cluster.

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