DFT / ATPG · All levels
Diagnosis & Yield Learning: Comparison Matrix
Comparison Matrix for Diagnosis & Yield Learning.
Comparison matrix
Channel count, masking, and compaction trade tester cost and diagnosis precision.
+------------------+----------------+----------------+----------------+
| Approach | Strength | Weakness | Best when |
+------------------+----------------+----------------+----------------+
| Conservative | safe | higher cost | ramp-sensitive |
| Balanced | practical | needs rigor | production |
| Aggressive | fast | escape risk | schedule pressure |
| Architecture | durable | slow | repeated pain |
+------------------+----------------+----------------+----------------+When to choose each approach
Tie approach to product risk and tester constraints
Interview traps
Optimizing one metric only
No regression ownership
Evidence comparison
DFT EVIDENCE MATRIX - Diagnosis & Yield Learning
+-------------------+------------------------+--------------------------+-------------------------+
| Evidence | Tells you | Does not prove | Next action |
+-------------------+------------------------+--------------------------+-------------------------+
| coverage report | fault detect trends | silicon root cause | inspect bucket details |
| pattern diff | generation changes | architecture quality | replay suspect patterns |
| timing/power report| test closure margin | diagnosis confidence | correlate fail logs |
| diagnosis summary | likely defect classes | ownership decision | assign action owner |
| signoff checklist | process completeness | correctness of evidence | peer review artifacts |
+-------------------+------------------------+--------------------------+-------------------------+DFT deep dive
Compression saves tester time only when diagnosis observability remains credible.
Concept diagram
COMPRESSION LOOP
EDT/decompressor -> compressed patterns -> compactor responses -> diagnosisMetric 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