Optimal+ GSA 2014
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The document discusses the challenges and strategies in improving quality measurement in testing processes, emphasizing the transition from measuring defects per million to billion. It highlights the complexities involved in analyzing big data for quality assurance, including the need for advanced outlier detection and escape prevention methods to reduce defects caused by human errors and test program issues. Strategies such as smart pairing of integrated circuits (ICs) based on quality indices and real-time data feed-forward for better decision-making are also outlined.
Optimal+ GSA 2014
- 1. Michael Schuldenfrei, CTO Leveraging Test Data for Quality GSA Quality Team Meeting December, 2014
- 2. © Optimal+ 2014 2 The Need Shifting from “Defects per Million” to “Defects per Billion”
- 3. © Optimal+ 2014 3 The Problem No Problem Found 32% Fab Process 28% Test Program 10% Test Operation 4% Test Equipment 26% RMA Source No Problem Found Fab Process Test Program Test Operation Test Equipment
- 4. © Optimal+ 2014 4 The Challenge BIGDATAEXPERTISE COSTTIME
- 5. © Optimal+ 2014 5 Big Data – Device DNA ECID ECID ECID ECID ECID WAT WS1 WS2 WAT WS1 WS2 WAT WS1 WS2 WAT WS1 WS2 WS3 WAT WS1 WS2 WS3 FT1 Burn in FT2 Example: One package contains: 5 dice x ~2 WS operations per die x ~1.2 iterations per operation x 3000 parametric measurements + 1000 per-site WAT measurements + 3000 FT measurements A DNA consisting ~35K measurements! An SLT lot with 5000 parts could have 150M historical measurements from hundreds of wafers & FT lots
- 6. © Optimal+ 2014 6 Back to Basics THE QUALITY QUESTION; IS “GOOD” REALLY GOOD?
- 7. Outlier Detection Geographic Parametric Escape Prevention Test program issues ATE issues Data Feed Forward (More intelligent decision making) Drift Smart Pairing © Optimal+ 2014 7 Quality Solutions
- 8. Outlier Detection © Optimal+ 2014 – All rights reserved 8
- 9. Optimal+ 2014 Company Confidential 9 Outlier Detection – Algorithms D-PAT: Dynamic Part Average Testing NNR: Nearest Neighbor Residual Z-PAT: Z-Axis Part Average Testing GDBN: Good Die in Bad Neighborhood Zonal: Low yield zone-based detection Final Test Post Final-Test operation and Based on Die-ID (ECID etc.) In real-time at Final-Test operation without Die-ID
- 10. Optimal+ 2014 Company Confidential 10 Cross-Operation Outlier Detection Cross-operational quality based on Die ID Contributing operations ETEST/PCM/WAT Wafer Sort Final-Test Burn-In System Level Test Example: E-Test based bin-switching performed post-Wafer Sort The ability to identify potential bad devices based on E-test data geographical analysis Bin switching occurs post-wafer sort Requires data-feed-forward within the supply chain
- 11. Escape Prevention © Optimal+ 2014 – All rights reserved 11
- 12. © Optimal+ 2014 12 Escape Prevention – ATE Freeze A freeze occurs when a tester instrument becomes “stuck” and repeatedly returns the same or similar result for a sequence of parts
- 13. © Optimal+ 2014 13 Escape Prevention – ATE / TP 13 The STDF “PRR.NUM_TESTS” field tells us the number of tests executed on the part. It should be relatively stable throughout the lot
- 14. © Optimal+ 2014 14 Escape Prevention – Test Ops Excessive probing – when operation ignores probe mark spec for a device and keeps on probing to get the yield
- 15. © Optimal+ 2014 15 Escape Prevention – Test Program Human error is one of the main contributors for test escapes and RMA. Here the PE commented a few blocks in the TP for debug and forgot to uncomment before production release: Traditional SBL is design to detect yield issues in which a specific bin count spikes. However human error can result in a drop to 0 which is missed. SBL SBL drop of soft bin 11 from ~3% to 0 following new TP revision
- 16. © Optimal+ 2014 16 Escape Prevention – Test Program Extremely loose test limits may mask real test performance problems ~95 Sigmas ~95 Sigmas
- 17. Advanced Quality Solutions © Optimal+ 2014 – All rights reserved 17
- 18. Implementations: Within the same test area (e.g. WS, FT, etc.) Between test areas (e.g. from WAT to WS to FT) Within a single subcon Between multiple subcons (hub and spoke) Real-time (test program integration) Offline bin-switching Example scenarios: Outlier Detection – drift analysis Pairing – cherry-picking for power & speed combinations Test program tuning SLT / Burn-in reduction © Optimal+ 2014 18 Data Feed Forward
- 19. © Optimal+ 2014 19 Data Feed Forward – Drift Database at subconTester 1. ECID Data 2. FT1 Measurements Test Program running FT2 operation Real-time data! No test time impact!
- 20. One or more numeric values representing the perceived quality of a part based on: Wafer geography (e.g. edge vs. center) Outlier detection rule inputs (e.g. GDBN, Z-PAT, D-PAT, etc.) Number of iterations to PASS Overall lot/wafer yield Equipment health during test Parametric test results from multiple operations Etc… © Optimal+ 2014 20 Quality Index Quality Index Lot/Wafer Yield etc. Quality Rule Inputs Wafer Geography
- 21. © Optimal+ 2014 21 “No Problem Found” Combinations of chips causing issues: IC3 IC2 PCB IC1
- 22. © Optimal+ 2014 22 Smart Pairing • New methodology to pair IC’s for optimal compatibility • Customer and suppliers agree on recipe for “Best Match” between IC’s (e.g. based on power consumption and speed) • “Quality Index” created based on manufacturing and test data to categorize chips • Data fed-forward to assembly to ensure IC’s pre-sorted into “buckets” based on Quality Index • MCPs and boards are assembled with well-matched components Grade A Grade B Grade C Grade A Grade B Grade C
- 23. Supreme Quality requires a comprehensive end-to-end approach which takes into account problems arising from: • Equipment • Test Process • Human Error • Material …and much more © Optimal+ 2014 23 Conclusions
- 24. Q&A Optimal+ 2014 Company Confidential 24