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
3. micro-transactions in emerging markets
4. GDE
5. GDE
6. GDE The competition $100 Android phone + $40 printer
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
8. GDE Failures in hardware
9. GDE Production pipeline
10. GDE Pick and Place https://www.youtube.com/watch?v=tn0EKtLOVx4 High speed https://www.youtube.com/watch?v=nah4BQ9y8IY
11. GDE Automated Optical Inspection (AOI) Chip tombstoning High resolution imaging for AOI
12. GDE Electrical Tests
13. GDE PCB tests
14. GDE Test jig v0.1
15. GDE Test jig v1.0
16. GDE Bed of nails Test jig v2.0 PCBA Multiplexer Power supply Measurement equipment Test jig software
17. GDE Test jig software
18. GDE Process changes Voltage
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" }, …… } }
20. GDE Test results saved in Big Query
21. GDE How many PCBA’s passed? 15% failure rate…
22. GDE Number of times a PCB is reworked
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
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%
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
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%
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%
28. GDE Change in process or quality
29. GDE Change in process or quality
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
31. GDE Failures in hardware
32. GDE The Future: Burn in and Ageing
33. GDE
34. GDE Thank You dale@nomanini.com dalehumby.com google.com/+DaleHumby @dalehumby cloud.google.com $500 promo code: gde-in
35. Experts Developer cloud.google.com $500 promo code: gde-in