Using big data to detect hardware manufacturing defects

How Nomanini reduces manufacturing defects by statistically analysing all components on our printed circuit board assemblies (PCBA's) in the factory.

Transcript

1. GDE
   Using big data to detect hardware
   manufacturing defects
   Dale Humby
   CTO, Nomanini
   Google Developer Expert for Cloud Platform
   

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3. micro-transactions in emerging markets
   

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4. GDE
   

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5. GDE
   

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6. GDE
   The competition
   $100 Android phone + $40 printer
   

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7. GDE
   Reduce hardware costs
   • Design: Use commodity parts
   • Supply chain: Volume purchasing, negotiation,
   30-90 day terms
   • Cheap to assemble (30% cost from assembly)
   • Low failure rate
   

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8. GDE
   Failures in hardware
   

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9. GDE
   Production pipeline
   

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10. GDE
    Pick and Place
    https://www.youtube.com/watch?v=tn0EKtLOVx4
    High speed https://www.youtube.com/watch?v=nah4BQ9y8IY
    

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11. GDE
    Automated Optical Inspection (AOI)
    Chip tombstoning
    High resolution imaging for AOI
    

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12. GDE
    Electrical Tests
    

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13. GDE
    PCB tests
    

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14. GDE
    Test jig v0.1
    

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15. GDE
    Test jig v1.0
    

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16. GDE
    Bed of nails
    Test jig v2.0
    PCBA
    Multiplexer
    Power supply
    Measurement
    equipment
    Test jig
    software
    

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17. GDE
    Test jig software
    

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18. GDE
    Process changes
    Voltage
    

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19. JSON
    GDE
    Test results
    POST /manufacturing/pcbas/{imei}/tests
    {
    "valid_time": "2015-10-10T18:00:00+00:00",
    "has_passed": true,
    "test_results": {
    "test_VUSB": {
    "measurements": [{
    "type": "VoltageDC",
    "result": 4.9020582
    }],
    "result": "PASS"
    }, ……
    }
    }
    

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20. GDE
    Test results saved in Big Query
    

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21. GDE
    How many PCBA’s passed?
    15% failure
    rate…
    

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22. GDE
    Number of times a PCB is reworked
    

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23. GDE
    {
    "valid_time": "2015-10-10T18:00:00+00:00",
    "has_passed": true,
    "test_results": {
    "test_time": "2016-02-18T18:30:42.724823",
    "tests": {
    "test_R127": {
    "measurements": [{
    "alias": “R127[TP46_TP65]",
    "line": 625,
    "type": “Resistance",
    "result": 860.55917
    }],
    "result": “PASS"
    },
    "test_R111": {
    "measurements": [{
    "alias": “BAT-GND[TP68]",
    "line": 618,
    "type": “Resistance",
    "result": 0
    }],
    "result": “PASS"
    },
    ... x 100’s of tests ...
    }
    SELECT * FROM [MANUFACTURE.pcba_test_results_flattened]
    Flatten JSON with User Defined Fnct
    

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24. GDE
    Top failing tests
    Top failing tests 724 %
    test_FUSE_2V5 92 13%
    test_R5_R6_divider 72 23%
    test_VBUS_CHARGE 70 32%
    test_VUSB_PROT 43 38%
    test_VDDANA 43 44%
    test_L3 40 50%
    test_VSYS 39 55%
    test_VDDIODDR 39 60%
    test_can_the_circuit_power_up 39 66%
    test_VDDCORE 39 71%
    test_R6 28 75%
    test_R128 18 78%
    test_R37 15 80%
    

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25. SQL
    GDE
    Stats of all the tests
    SELECT
    test_name,
    alias,
    result,
    COUNT(result) AS number,
    type AS units,
    MIN(value) AS min,
    NTH(5, QUANTILES(value ,101)) fifth,
    AVG(value) AS avg,
    NTH(50, QUANTILES(value ,101)) median,
    NTH(95, QUANTILES(value ,101)) ninety_fifth,
    MAX(value) AS max
    FROM [MANUFACTURE.pcba_test_results_flattened]
    GROUP BY
    test_name, alias, result, units
    ORDER BY
    number DESC
    

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26. GDE
    Analysis of R32
    test_name result numberunits min fifth avg median ninety_fifth
    max
    test_R32 PASS 542 Resistance 9880.428 9896.257 9929.666 9929.325 9959.646 9996.525
    test_R32 FAIL 9 Resistance 0 0 7.70E+37 9.90E+37 9.90E+37 9.90E+37
    % Error 1.20% 1.04% 0.70% 0.71% 0.40% 0.03%
    

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27. GDE
    Vsys (3V3)
    test_name result numberunits min fifth avg median ninety_fifth
    max
    test_VSYS P
    A
    S
    S 512 VoltageDC 3.281589 3.287578 3.297513 3.297278 3.307268 3.317785
    test_VSYS FAIL 39 VoltageDC -0.00198 -0.00163 0.065324 0.075967 0.082376 0.082787
    % Error -0.56% -0.38% -0.08% -0.08% 0.22% 0.54%
    

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28. GDE
    Change in process or quality
    

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29. GDE
    Change in process or quality
    

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30. R
    GDE
    Shift in production process
    > t.test(r32$old, r32$new)
    Welch Two Sample t-test
    data: r32$old and r32$new
    t = -25.459, df = 40.49, p-value < 2.2e-16
    alternative hypothesis: true difference in means is not equal to 0
    95 percent confidence interval:
    -78.96871 -67.35696
    sample estimates:
    mean of x mean of y
    9929.666 10002.829
    

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31. GDE
    Failures in hardware
    

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32. GDE
    The Future: Burn in and Ageing
    

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33. GDE
    

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34. GDE
    Thank You
    dale@nomanini.com
    dalehumby.com
    google.com/+DaleHumby
    @dalehumby
    cloud.google.com
    $500 promo code: gde-in
    

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35. Experts
    Developer
    cloud.google.com
    $500 promo code: gde-in
    

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