Diagnosis & Yield Learning: Mechanism

Mechanism to understand

Mechanism for Diagnosis & Yield Learning focuses on diagnosis resolution, top failing buckets, yield learning turnaround. The goal is to convert metric movement into mechanism, owner, and release decision.

Fail logs and diagnosis map failing signatures back to likely defect sites, enabling systematic yield learning loops with design and process teams. Think of DFT as a quality pipeline where setup quality determines what silicon evidence means.

• Identify where controllability/observability is introduced.

• Identify legal constraints and mode assumptions.

• Identify failure class: architecture, constraints, physical, or silicon.

Layered view

DFT CLOSURE FLOW - Diagnosis & Yield Learning

scan/test architecture
        |
        v
ATPG constraints + fault models
        |
        v
pattern generation + compression
        |
        v
timing/power/physical validation
        |
        v
silicon diagnosis and release signoff

Debug rule: always state metric, run tags, and owning team with any claim.

Diagnosis feedback loop

fail logs -> suspect list -> diagnosis buckets -> yield pareto -> design/process action

The loop is valuable only with consistent signatures and tagging.

Layer responsibilities

DFT OWNERSHIP LAYERS - Diagnosis & Yield Learning

layer              owns                         failure mode
----------------   --------------------------   -------------------------
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 slip

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.

Mechanism deep dive

Fail logs and diagnosis map failing signatures back to likely defect sites, enabling systematic yield learning loops with design and process teams.