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Course contents

  • How to Use This Course
  • AMS Metrics Reference
  • AMS Interface Glossary
  • Reading Path
  • Analog-Digital Boundaries61
  • PLL & DLL Clocking61
  • SerDes & High-Speed I/O61
  • ADC / DAC Interfaces61
    • ADC Digital Wrapper
    • Sample Timing Contract
    • Calibration Hooks
    • Data Validity Debug
      • Data Validity Debug: Mechanism
      • Data Validity Debug: Inputs & Outputs
      • Data Validity Debug: Reports & Metrics
      • Data Validity Debug: Debug Playbook
      • Data Validity Debug: Worked Example
      • Data Validity Debug: Pitfalls & Red Flags
      • Data Validity Debug: Interview Drills
      • Data Validity Debug: Review Checklist
      • Data Validity Debug: Theory Deep Dive
      • Data Validity Debug: Design Space
      • Data Validity Debug: Expanded Case Study
      • Data Validity Debug: Step-by-Step Walkthrough
      • Data Validity Debug: Comparison Matrix
      • Data Validity Debug: Software / Programmer View
      • Data Validity Debug: Silicon PPA Impact
    • ADC / DAC Interfaces: Tricky Q&A
  • Mixed-Signal Integration61
  • Noise & IR at the Boundary61
  • AMS Interface Interview Prep61
  • AMS Interface Cheatsheet
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‹Data Validity Debug
  1. Topics
  2. Analog & Mixed-Signal
  3. ADC / DAC Interfaces
  4. Data Validity Debug
  5. Data Validity Debug: Reports & Metrics

AMS Interface · All levels

Data Validity Debug: Reports & Metrics

Reports & Metrics for Data Validity Debug.

On this page

    Reports and metrics

    Reports & Metrics for Data Validity Debug focuses on invalid sample bursts, clipping incidents, stale-data escapes. The goal is to connect observed symptom to boundary mechanism, ownership, and signoff risk.

    Reports convert invalid sample bursts, clipping incidents, stale-data escapes into release decisions. A single average metric is insufficient; use tails, mode breakdowns, and trend under controlled conditions.

    Metric movement

    Diagram
    METRIC GRAPH — invalid sample bursts, clipping incidents, stale-data escapes
    
    health / margin
      ^
      |                         target
      |                       - - - - - - -
      |                  o post-fix
      |              o
      |         o baseline
      |    o failing run
      +--------------------------------------> debug iteration
        repro setup    root-cause proof    release candidate
    
    Readout:
      - compare only equivalent mode, payload, clock, and environment tags
      - separate symptom stabilization from true mechanism closure

    Distribution view

    Diagram
    METRIC DISTRIBUTION — Data Validity Debug
    
    occurrence
      |      ***
      |    *******
      |  ***********
      |*************  <- left tail (worst boundary excursions)
      +--------------------> margin / stability / error metric
      bad                 nominal
    
    Fix the tail first; average value can hide integration escapes.
    • Track invalid sample bursts, clipping incidents, stale-data escapes by mode and stress condition.

    • Report p95/p99 or worst-case tail when intermittent escapes matter.

    • Store metric with exact artifact and configuration tags.

    • Separate symptom stabilization from mechanism closure.

    AMS deep dive

    Wrapper and sampling contracts determine data trustworthiness.

    Concept diagram

    Diagram
    CONVERTER DATA FLOW
    
    sampling clock -> wrapper -> sync boundary -> system consumer

    Metric graph

    Diagram
    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.

    Reading reports

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    Next: Data Validity Debug: Debug Playbook

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    See also

    • Data Validity Debug: Mechanism
    • Data Validity Debug: Worked Example
    • Data Validity Debug: Pitfalls & Red Flags