Design space exploration

For Diagnosis & Yield Learning, release options trade quality, tester cost, and schedule risk.

Option A - conservative

• Conservative quality: helps debug confidence

• Risk: higher test cost

• Validate with: first production ramps

Option B - balanced

• Balanced release: helps quality and cost

• Risk: needs discipline

• Validate with: mainstream products

Option C - aggressive

• Aggressive schedule: helps faster closure

• Risk: escape risk

• Validate with: late tapeout pressure

Option D - architecture change

• Architecture change: helps long-term quality

• Risk: schedule hit

• Validate with: chronic recurring failures

DESIGN SPACE - Diagnosis & Yield Learning
quality <-> tester cost <-> schedule

Design pitfalls

• No ownership for quality gap

• Fixing one metric while regressing another

Tradeoff curve

BEFORE / AFTER - Diagnosis & Yield Learning

metric quality
  ^
  |                    --- release target
  |      o regressed
  |           o baseline
  |                o after fix
  +-------------------------------> closure iteration

Prove quality, timing, and test-power all moved safely.

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