Diagnosis & Yield Learning turns DFT intent into measurable release confidence. Fail logs and diagnosis map failing signatures back to likely defect sites, enabling systematic yield learning loops with design and process teams. The senior challenge is proving whether a metric move came from real quality gain, setup drift, or hidden regression.
When diagnosis resolution, top failing buckets, yield learning turnaround moves, can you identify mechanism, evidence quality, owner, and the minimum safe next action?
DFT CLOSURE FLOW - Diagnosis & Yield Learning
scan/test architecture
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ATPG constraints + fault models
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pattern generation + compression
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timing/power/physical validation
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silicon diagnosis and release signoff
Debug rule: always state metric, run tags, and owning team with any claim.Draw the causal flow before opening tools. Use these diagrams to anchor architecture, constraints, and silicon behavior discussions.
fail logs -> suspect list -> diagnosis buckets -> yield pareto -> design/process action
The loop is valuable only with consistent signatures and tagging.DFT FLOW - Diagnosis & Yield Learning
scan insertion -> chain stitch -> compression map -> ATPG -> tester apply -> diagnosis
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controllability shift balance channel use coverage silicon correlation
Primary metric: diagnosis resolution, top failing buckets, yield learning turnaroundDFT OWNERSHIP LAYERS - Diagnosis & Yield Learning
layer owns failure mode
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rtl/architecture scanability hooks uncontrollable logic
atpg/constraints legal pattern intent aborts, low coverage
physical/clocking chain route + test clocks shift hold/timing escapes
tester/program pattern apply integrity false binning / bad fails
quality signoff release criteria escapes or schedule slipPrimary metric: diagnosis resolution, top failing buckets, yield learning turnaround.
Primary artifact: diagnosis report, fail-log bucket table, yield pareto dashboard.
Owners to bring into review: product test owner, yield engineer, DFT lead.
One failing signature and one reduced reproduction path.
Exact run tags for constraints, patterns, and tester program.
OWNERSHIP MAP - Diagnosis & Yield Learning
artifact owner
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architecture/report product test owner
constraints/setup yield engineer
physical/test DFT lead
Name an owner for each failing metric cluster.Each topic includes mechanism, inputs/outputs, reports, debug, worked example, pitfalls, interview, checklist, theory, design space, expanded case study, walkthrough, comparison matrix, software view, and silicon impact.
State metric and run tags with every claim.
Connect every fix to a regression matrix.
Treat quality, timing, and power as coupled.
Coverage-centric decisions without legality checks.
Pattern changes without tester correlation.
Release calls without owner signoff.
Compression saves tester time only when diagnosis observability remains credible.
COMPRESSION LOOP
EDT/decompressor -> compressed patterns -> compactor responses -> diagnosisPATTERN vs COVERAGE
coverage up -> pattern count up
compression up -> pattern count down (until aliasing risk)compression ratio dashboard
pattern count trend
X-source report
diagnosis bucket summary
Compactor overflow plus unknown inflation caused false diagnosis; masking policy and channel map corrected.
Separate X issues from silicon defects
Replay failing patterns uncompressed
Track tester memory budget
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.