Inputs & Outputs for Scenario: Silicon Mismatch focuses on silicon fail reproducibility, diagnosis confidence, lab-to-ATE correlation. 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 decisionDFT FLOW - Scenario: Silicon Mismatch
scan insertion -> chain stitch -> compression map -> ATPG -> tester apply -> diagnosis
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controllability shift balance channel use coverage silicon correlation
Primary metric: silicon fail reproducibility, diagnosis confidence, lab-to-ATE correlationOWNERSHIP MAP - Scenario: Silicon Mismatch
artifact owner
---------------- -----------------
architecture/report silicon bring-up owner
constraints/setup DFT owner
physical/test ATE owner
Name an owner for each failing metric cluster.Senior DFT interviews test tradeoff judgment under imperfect data.
INTERVIEW ANSWER LOOP
state metric -> explain mechanism -> request evidence -> propose fix -> list regressionANSWER QUALITY
mechanism clarity + ownership clarity + regression disciplinewhiteboard rubric
scenario scorecard
trap checklist
follow-up depth index
Candidate improved from tool-only answers to mechanism-first narratives by using metric->artifact->decision template.
Always state assumptions
Name cross-team owners
Discuss risk and fallback plan
Ask: what evidence proves this DFT decision is safe for production?
State metric, lot/corner context, and pattern tag with every claim.
Treat timing, power, and quality as one signoff problem.
Chasing coverage without legality checks.
Ignoring test-power side effects of pattern changes.
Debugging silicon without reproducible tags.
Silicon mismatch scenarios test whether engineers can correlate ATPG assumptions, tester setup, and physical behavior before escalating.
Metric: silicon fail reproducibility, diagnosis confidence, lab-to-ATE correlation
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