Diagnosis & Yield Learning: Silicon PPA Impact

Silicon impact

Compressed signatures are only useful when X behavior is controlled.

Area drivers

• compression logic footprint

• hold buffers from scan paths

Power drivers

• shift toggling peaks

• capture burst stress

Timing impact

• test-clock skew

• shift/capture path sensitivity

PD consequences

• chain route detours

• placement hotspots near test logic

Verification burden

• pattern replay correlation

• diagnosis reproducibility

SILICON IMPACT - Diagnosis & Yield Learning
area/power/timing/yield tradeoff

Takeaways

• Signoff needs silicon evidence

• Ownership clarity accelerates closure

Design option snapshot

CHAIN BALANCE - Diagnosis & Yield Learning

chain length
  ^
  |      o        o
  |   o    o  o
  | o
  +-----------------------------> chain index

Tight spread reduces shift time and hold-fix burden.

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