AOI Parameter and Board-Flow Optimization

Optimizes automated optical inspection settings and PCB board-flow using AI to reduce false calls, improve first-pass yield at the source, and support broader shop-floor automation.

The Problem

“AOI Parameter and PCB Board-Flow Optimization for Higher First-Pass Yield”

Organizations face these key challenges:

1

AOI recipes are manually tuned and drift over time across products and lines

2

False calls consume operator review capacity and hide true process issues

3

Board-flow data is fragmented across MES, AOI, SPI, placement, reflow, and repair systems

4

Root-cause analysis is slow because yield loss emerges from multi-step process interactions

Impact When Solved

Reduce AOI false-call rate through context-aware parameter recommendationsIncrease first-pass yield by linking inspection outcomes to upstream process conditionsShorten engineering time spent on recipe tuning and root-cause analysisLower rework, review, and line-stoppage costs

The Shift

Before AI~85% Manual

Human Does

  • •Review AOI false-call logs and defect trends by product family and line
  • •Manually tune inspection thresholds, lighting, and recipe settings through trial-and-error
  • •Pull board-flow history from MES, SPI, placement, reflow, and repair records to investigate yield loss
  • •Adjust routing, process windows, and rework loops based on retrospective engineering analysis

Automation

    With AI~75% Automated

    Human Does

    • •Approve recommended AOI recipe changes and board-flow interventions before deployment
    • •Set quality guardrails, yield targets, and limits for automated adjustments
    • •Review high-risk exceptions, unexpected yield shifts, and suspected model errors

    AI Handles

    • •Unify board-level event history and monitor false-call, miss-risk, and yield patterns across lines
    • •Generate ranked AOI parameter recommendations by product context, line, and defect class
    • •Identify upstream process and routing patterns that predict escapes, false calls, or first-pass yield loss
    • •Trigger bounded recipe or route adjustments and measure outcome impact for continuous optimization

    Operating Intelligence

    How it works

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence88%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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