Data Validity Debug: Design Space

Design space exploration

For Data Validity Debug, closure options trade robustness, schedule, and margin.

Option A — conservative

• Conservative boundary guard: helps high robustness

• Risk: area and schedule cost

• Validate with: first-silicon risk

Option B — balanced

• Balanced integration policy: helps practical closure

• Risk: needs discipline

• Validate with: production ramp

Option C — aggressive

• Aggressive late ECO: helps quick symptom relief

• Risk: cross-domain regressions

• Validate with: deadline crunch

Option D — structural

• Architecture-level reshaping: helps durable fix

• Risk: long lead time

• Validate with: repeat offenders

DESIGN SPACE — Data Validity Debug
robustness <-> performance <-> schedule
choose by invalid sample bursts, clipping incidents, stale-data escapes on product-representative scenarios

Design pitfalls

• Mode coverage too shallow

• Fixing symptom without ownership alignment

Tradeoff curve

BEFORE / AFTER — Data Validity Debug

health score
  ^
  |                     --- target
  |         o regressed
  |               o before fix
  |                    o after bounded fix
  +--------------------------------------> debug iteration

Prove signal quality, timing margin, and integration safety together.

AMS deep dive

Wrapper and sampling contracts determine data trustworthiness.

Concept diagram

CONVERTER DATA FLOW

sampling clock -> wrapper -> sync boundary -> system consumer

Metric graph

INVALID SAMPLE BURSTS

time --->  _|¯¯|__|¯¯¯|____|¯|_

Reports and artifacts

• sample validity ratio

• calibration convergence

• CDC audit

• overflow/stale sample counters

Mini case study

Mode transition changed sample phase and invalidated wrapper framing assumptions.

Debug branches

• Sample timing contract

• Calibration hook behavior

• Wrapper CDC integrity

Senior review question

Ask: what boundary condition proves this topic is actually closed?

Key takeaways

• State boundary, mode, and evidence tag with every claim.

• Always align analog, digital, and physical owners before signoff decisions.

Common pitfalls

• Fixing averages while tails still fail.

• Skipping package/supply evidence in jitter or SerDes issues.

• Shipping with waivers that lack owner and expiration criteria.

Principal AMS review addendum

Data validity must combine framing correctness, saturation/overflow signaling, and synchronization sanity so software trusts converter samples.

Metric: invalid sample bursts, clipping incidents, stale-data escapes