Diagnosis & Yield Learning: Interview Drills

Interview drills

Interview Drills 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.

PROMPT
You observe diagnosis resolution, top failing buckets, yield learning turnaround on Diagnosis & Yield Learning. Walk through diagnosis and release decision.

STRONG ANSWER
1. States context and run tags.
2. Explains mechanism: Fail logs and diagnosis map failing signatures back to likely defect sites, enabling systematic yield learning loops with design and process teams.
3. Requests diagnosis report, fail-log bucket table, yield pareto dashboard.
4. Proposes minimal fix and regression.

WEAK ANSWER
Jumps to tool switches without evidence quality, ownership, or regression plan.

Diagram to draw on the whiteboard

Diagnosis feedback loop

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

The loop is valuable only with consistent signatures and tagging.

Root-cause narrative

ROOT-CAUSE TREE - Diagnosis & Yield Learning

diagnosis resolution, top failing buckets, yield learning turnaround regresses
        |
  setup changed?
    /        \
  yes         no
  |            |
constraint    silicon or
or ATPG       physical/test path
 /    \          |
SDC   model    chain/clock/power/diagnosis
diff  diff     isolate first failing signature

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