Diagnosis & Yield Learning: Inputs & Outputs

Inputs and outputs contract

Inputs & Outputs 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.

Treat these as a release contract. Ambiguity here creates expensive debug loops because teams optimize against different assumptions.

INPUTS
  - scan/ATPG architecture and constraints
  - fault model and quality target policy
  - pattern generation config + tester limits
  - timing/power/physical assumptions

OUTPUTS
  - quality metrics and closure status
  - signed artifacts and owner approvals
  - diagnosis evidence for residual risk
  - release, waiver, or escalation decision

Flow sequence

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

Ownership map

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