convanit

convanit
Menü
Yield und Datenanalyse

convanitYield und Datenanalyse

    • DE
    • EN
  • Overview
  • Contact
  • Competencies
  • Imprint
  • c-Alice
  • Project examples
  • Team
  • Partner
  • References
convanit

Improvement of Defect Engineering Methods

What we mean

Early detection of production related quality issues and proactive responses ensure low yield loss.

How we do it

Through methods like sampling, control strategy, defect to yield correlation, defect reporting, defect cataloguing , statistical analysis of defect data in relation to all other production relevant data we can create the most effective approach for you.

  • Yield/Quality
  • Consulting
  • Data Integration
  • Implementation
  • Organization
  • Data Analytics
    • Workshops
    • Coaching
    • Potential Analysis
    • Concept development
    • Yield and Reliability improvement
    • Defect Engineering
    • Process control methodology
    • Toolmonitoring
    • Critical Process parameter
    • Yield correlation and prediction, other methods
    • Correlation analysis
    • Signature- und image analysis
    • Machine Learning / Artifical Intelligence
    • Data analysis and reporting application
    • Team development
    • Sustainable change
    • Feasability (POC)
    • Workflow Design
    • Method- and tool selection
    • Prototyping 
    • costumer specific solutions
    • Implementation of new methods

    Example 1

    Chip based Defect to Yield Correlation

    kill-rate-berechnung

    ‚Contigency table‘ Methode / Berechnung von Kill Rate & Defect Related Loss

    Requirements

    quality of inspection recipes, defect filter: large defects, Adder, Defects of interest per inspection step, low parametric yield loss

    Process step A

    defects-defect-engineering-projektbeispiel
    kill-rate-defect-engineering-projektbeispiel

    Kill Rate: 21%
    Defect Related Loss: 3,1%
    (All Binsorts)

    Example 2

    Yield correlation with intitial large detect filters

    large-defects-defect-engineering-projektbeispiel

    4 weeks data, one product, standard wafer, Large defects only, special defect related binsorts used, low yielding wafer excluded

    Example 3

    Large Defect Filter definition and optimization

    defect-filter-optimization-before-projektbeispiel

    Filter:
    Volume > 35 & Grade > 50

    Initial definition of Large Defect filter by inspection tool raw parameters: Volume/ Grade (DF inspection), DSIZE (BF inspection)

    defect-filter-optimization-volume-kill-rate
    defect-filter-optimization-grade-kill-rate

    New Filter:
    Volume > 25 & Grade > 50

    Optimization of Large Defect filter by kill rate and defective related yield loss analysis

    Initial definition of Large Defect filter by inspection tool raw parameters: Volume/ Grade (DF inspection), DSIZE (BF inspection)

    Optimization of Large Defect filter by kill rate and defective related yield loss analysis

    Competencies and Keywords

    Overview

    convanit

    Beratung zu Yield und Datenanalyse

    Impressum und Datenschutz

    Kontakt