Pattern Volume Tradeoffs: Worked Example

Worked example

Worked Example for Pattern Volume Tradeoffs focuses on pattern count, tester memory usage, test application time. The goal is to convert metric movement into mechanism, owner, and release decision.

A release review shows regression on pattern count, tester memory usage, test application time. The strongest first move is to freeze evidence, isolate one failing bucket, and prove mechanism before changing flow knobs.

Sequence under inspection

DFT FLOW - Pattern Volume Tradeoffs

scan insertion -> chain stitch -> compression map -> ATPG -> tester apply -> diagnosis
      |                |                |            |             |
 controllability   shift balance    channel use   coverage     silicon correlation

Primary metric: pattern count, tester memory usage, test application time

Pattern volume tradeoff

coverage target up  -> pattern count up
X-masking strict   -> pattern count up
compression ratio up -> pattern count down (to a limit)

Choose target with ATE budget in loop.

1. Capture failing report and exact run tags.

2. Tag scenario (mode, lot/corner, pattern class).

3. Trace first dependency that changed.

4. Compare against ATPG pattern summary, tester memory estimate, runtime trend.

5. Choose one reversible fix and predefine regression checks.

Did the fix work?

BEFORE / AFTER - Pattern Volume Tradeoffs

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

Pattern volume is a tradeoff between coverage closure aggressiveness, X-handling policy, and compression settings constrained by tester resources.

Metric: pattern count, tester memory usage, test application time