Increase production yield

Improve first pass yield in electronics with artificial intelligence in manufacturing

QualityLine increases first pass yield by quickly identifying any errors in the manufacturing process. The patented technology automatically integrates, collects and analyses any type of manufacturing data collected during your manufacturing process:

  • SMT machines
  • AOI, Flying probe, SPI, X-Ray
  • Automated test equipment
  • Repair/rework
  • ERP/EMS
  • Machines
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Ready to optimize your first pass yield?

Learn how to apply AI to maximize first pass yield in electronics manufacturing

How does it work?

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QualityLine AI technology automatically integrates, analyzes, and visualizes all your manufacturing data

EMS quality and yield increased 30% in the first year with AI advanced manufacturing analytics. 

  • KPI’s analytics (First Pass Yield, Rolled Throughput Yield, Total Yield, DPMO, Failure rate.
  • Products quality  – CP/Cpk, SPC and detailed analytics to single unit
  • Cross correlation between product parameters

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Predictive failures rate and machine downtime & Automatic Alerts for abnormal issues and Manufacturing Analytics Reports

  • Testing Stations stations performance and availability
  • Repairs Analytics
  • Retests Analytics
  • SMT Analytic
  • Scheduling of automated reports and SMS/email alerts to prevent degradation in the manufacturing process.

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Tips for best analytics results

we recommend that your data will include:

Data for Testing Analytics

  • Serial Number (SN) of each unit
  • Product Part Number
  • Testing station Name/number
  • Operator Name (if exist)
  • Timestamp (time/date)
  • Status of the testing unit (PASS/FAIL)
  • Process Name (if exist)
  • Parameter name (the names of each tested parameter within each test session)
  • Lower Limit of each test
  • Higher Limit of each test
  • Result of each test
  • Test Status – the result of each test – pass/fail (if exist)
  • Test Duration (if exist)
  • Measuring Units (if exist)

Data for Repairs Analytics

  • Serial Number (SN) of each unit
  • Product Part Number
  • Technician name or number  (if exist)
  • Failed parameter of symptom found in the testing
  • The faulty component or material found defective during repair (component number and also reference designator)
  • The failure code that was found by the technician (broken component, cold soldering…)
  • Text description of the problem found (if exists)
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    Unlock the value of your data
    Global end-to-end control

    Automated and fast integration of any manufacturing data source

    QualityLine experts have experience with more than 1500 production lines around the world and more than 30 years in the manufacturing industry.

    We want to use our knowledge and experience to help you improve your manufacturing efficiency and product quality.

    Our machine learning and artificial intelligence technology will make this possible.

    tips_icon

    Tips for best analytics results

    we recommend that your data will include:

    Data for Testing Analytics

    • Serial Number (SN) of each unit
    • Product Part Number
    • Testing station Name/number
    • Operator Name (if exist)
    • Timestamp (time/date)
    • Status of the testing unit (PASS/FAIL)
    • Process Name (if exist)
    • Parameter name (the names of each tested parameter within each test session)
    • Lower Limit of each test
    • Higher Limit of each test
    • Result of each test
    • Test Status – the result of each test – pass/fail (if exist)
    • Test Duration (if exist)
    • Measuring Units (if exist)

    Data for Repairs Analytics

    • Serial Number (SN) of each unit
    • Product Part Number
    • Technician name or number  (if exist)
    • Failed parameter of symptom found in the testing
    • The faulty component or material found defective during repair (component number and also reference designator)
    • The failure code that was found by the technician (broken component, cold soldering…)
    • Text description of the problem found (if exists)

    AI Analytics: Improve Your First Pass Yield by integrating all your data into a unified digital twin database

     “First pass yield” (FPY), is defined as the % of units that pass the first time the test of a particular process divided by the total number of units tested during the period. In other words, it measures the quality and efficiency of a manufacturing process. 

    The higher the FPY, the more consistent and reliable the process. In order to improve production yields and efficiency, it is important to gain full visibility into all stages of the manufacturing process.

    Using AI analytics tools to determine the proportion of finished units that pass inspection during product testing is the most effective way to accomplish this. Let’s look at the reasons why.

    Data Collection – Gathering the Right Information

    QualityLine’s AI manufacturing Analytics and automated data integration maximizes manufacturing efficiency and product quality as it continuously collects the entire manufacturing data into a unified digital twin database. Data sources from multiple global locations are then harmonized and analyzed in real time.

     

    By ensuring that you incorporate all of your manufacturing data into the formula, you will be able to achieve a correlation between the data that will provide the most accurate insights for identifying and resolving issues.

    Monitor KPIs in real time to improve the detection of faulty units.

     

    The first pass yield can be calculated manually, but it is best tracked with real-time production data collected by AI manufacturing analytics. In order to achieve a high and consistent FPY, manufacturers should monitor their output in real time.

    QualityLine AI analytics delivers ongoing monitoring & optimization of yield and is powered by automatic alerts in case of issues, prediction reports and more. 

    QualityLine continuously monitors the yield of a factory by ensuring that all data sources from any factory are combined, integrated and analysed. Based on the statistics of QualityLine customers, First Pass Yield was improved by 30% within the first year after using the AI analytics.