Diagnosis & Yield Learning: Reports & Metrics

Reports and metrics

Reports & Metrics 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 report should support a release decision for diagnosis resolution, top failing buckets, yield learning turnaround. One headline number is rarely enough without setup tags and bucket-level evidence.

Metric movement

METRIC GRAPH - diagnosis resolution, top failing buckets, yield learning turnaround

quality metric
  ^
  |                         target
  |                       - - - - - - -
  |                  o after fix + regression
  |              o
  |         o baseline
  |    o regressed run
  +--------------------------------------> closure iteration
    input audit     focused fix      signoff review

Readout:
  - explain what moved, why it moved, and who approved it

Distribution view

PATTERN HISTOGRAM - Diagnosis & Yield Learning

pattern count
  |      ***
  |    *******
  |  ***********
  |*************  <- high-volume tail (optimize here)
  +--------------------> pattern buckets
   smoke  stuck-at  transition  diagnosis

Balance quality and tester limits together.

• Track diagnosis resolution, top failing buckets, yield learning turnaround by context and pattern class.

• Review trend and bucket detail together.

• Keep quality, timing, and power metrics in one dashboard.

• Store each metric next to the artifact and run tag.

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.

Reading reports