Diagnosis & Yield Learning: Step-by-Step Walkthrough

Step-by-step analysis walkthrough

Use when you own Diagnosis & Yield Learning in a DFT closure review.

1. Open release criteria and failing metric dashboard.

2. Partition failures by pattern family and operating context.

3. Inspect constraints, clocks, masks, and unknown handling.

4. Check architecture assumptions against implementation reality.

5. Review diagnosis evidence for repeatability.

6. Assign owner and propose smallest high-confidence change.

7. Run timing, power, and quality regressions.

8. Capture final signoff or waiver decision.

Artifacts to collect

• diagnosis report, fail-log bucket table, yield pareto dashboard

• pattern tag manifest

• tester program revision

• signoff checklist

• owner tracker

Decision memo template

DFT DECISION MEMO - Diagnosis & Yield Learning
scenario:
metric:
hypothesis:
fix:
regression:
owners: product test owner, yield engineer, DFT lead

Reference visuals

Diagnosis feedback loop

fail logs -> suspect list -> diagnosis buckets -> yield pareto -> design/process action

The loop is valuable only with consistent signatures and tagging.

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