Diagnosis & Yield Learning: Worked Example

Worked example

Worked Example 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.

A release review shows regression on diagnosis resolution, top failing buckets, yield learning turnaround. The strongest first move is to freeze evidence, isolate one failing bucket, and prove mechanism before changing flow knobs.

Sequence under inspection

DFT FLOW - Diagnosis & Yield Learning

scan insertion -> chain stitch -> compression map -> ATPG -> tester apply -> diagnosis
      |                |                |            |             |
 controllability   shift balance    channel use   coverage     silicon correlation

Primary metric: diagnosis resolution, top failing buckets, yield learning turnaround

Diagnosis feedback loop

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

The loop is valuable only with consistent signatures and tagging.

1. Capture failing report and exact run tags.

2. Tag scenario (mode, lot/corner, pattern class).

3. Trace first dependency that changed.

4. Compare against diagnosis report, fail-log bucket table, yield pareto dashboard.

5. Choose one reversible fix and predefine regression checks.

Did the fix work?

BEFORE / AFTER - Diagnosis & Yield Learning

metric quality
  ^
  |                    --- release target
  |      o regressed
  |           o baseline
  |                o after fix
  +-------------------------------> closure iteration

Prove quality, timing, and test-power all moved safely.

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