Why Quality Depends on Big Data

Transcription

1 Why Quality Depends on Big Data Korea Test Conference Michael Schuldenfrei, CTO

2 Who are Optimal+? 2

3 Company Overview Optimal+ provides Manufacturing Intelligence software that delivers realtime, big data analytics for distributed semiconductor manufacturing operations Our solutions transform manufacturing test data into actionable intelligence that improves yield, quality and productivity with full supply chain visibility 3

4 Top Companies Run Optimal+ Ten years managing Big Data for the world s leading semiconductor manufacturers 4

5 Proven Results and Strong ROI are processed and approved for delivery to market each year using Optimal+ enables any semiconductor company to seamlessly network its endless stream of fragmented Big Data and convert it into a unified Big Picture 2 in product yield recovery based solely on test over traditional TTR methods in operational efficiency & productivity improvements 50 decrease in test escapes, improving quality and reducing RMAs 5

6 Big Data IT Industry Perspective 6

7 The Big Data Revolution Three V s Volume The amount of data being handled is orders of magnitude larger than the amount of data traditional databases can handle. Velocity Data arrives fast and needs to be processed quickly. It is most useful when decision making can be performed on the data in real-time. Variety A wide variety of sources contain information that is useful to organizations. This goes beyond traditional structured data in databases and includes media files, log files, sensor data and much more. Value What can you do with the data and does the value you get justify the cost to store and manage the data? 7

8 Big Data Sources Mobile ERP Web CRM Big Data Social Sensor Audio Logs Video

9 And What Is It Used For? Marketing Advertising Fraud Detection Intelligence Tax Evasion Research and Engineering? 9/3/2015 9

10 Big Data Solutions Many Players NoSQL Map / Reduce Hadoop Vertica Mongo DB Cloudera Column Oriented Hortonworks HBase Cassandra Redis Exasol ParAccel Impala IBM Infosphere Pig Shared Nothing Voldemort Memory Grid Sybase IQ Teradata Commodity Servers Horizontal Scalability HDFS SAP Hana B.A.S.E. Shared Everything Splunk Key-Value Graph Stores 10

11 Big Data for Semiconductor Test 11

12 Why does it Matter to Us? Current databases are large Up to 100TB/Year at large customers x4 growth in the last 2 years Seeing rapid increase in database size due to: Longer retention periods (e.g. for RMA) More operations (E-test, SLT) Data log growth Expecting more complex queries Data mining Cross operation analysis 12

13 Some Numbers (One Large Fabless/IDM) >10,000 tester data logs per day >3,000 additional files from other sources ~2,000 parts tested in each data log ~3,000 parametric measurements per part ~100 GB per day raw data ~50 GB compressed data loaded/purged a day 13

14 Structured vs. Non-Structured CRM Databases MES Structured Data XML JSON Log Files Spreadsheets Video Social Media Audio Unstructured Data Text Documents ERP Parametric Test Measurements Blogs Web Sites 14

15 How Big is Big? ECID ECID ECID ECID ECID FT1 Burn in FT2 WAT WS1 WAT WS1 WS2 WAT WS1 Example: One package contains: 5 dice x ~1.2 WS operations per die x ~1.2 iterations per operation x 3000 parametric measurements per-site WAT measurements FT measurements A DNA consisting ~25K measurements! WAT WS1 WAT WS1 An SLT lot with 5000 parts could have 100M historical measurements from hundreds of wafers & FT lots 15

16 What Could You Do With It? Examples RMA Analysis Identify predictors for FT, SLT or RMA fallout Perform bivariate correlations on all possible combinations of tests to identify bivariate outliers Define a rule to use the results to prevent fallout Parametric Stability Monitor Monitor every test parameter to detect unstable results or drifts (e.g. using Cpk) which are typically masked when looking solely at binning results Characterization and Test Conditions Include additional dimensions in analysis such as test conditions or custom attributes both in characterization and production phases of a product s life cycle Smart Filtering Filter large volumes of parametric measurements to focus on the parameters which matter. For example, filter out low entropy, bi-modal or low Cpk tests before performing complex analysis ANOVA Search across multiple dimensions to detect outlier equipment, tests, bins, etc. 16

17 The Challenges 17

18 Garbage In Garbage Out The 3 C s for data collection Complete Clean Consistent 18

19 Making it Actionable To be Actionable, data must: Be available quickly Be processed immediately and automatically Be connected to business processes 19

20 Processing Raw Data Many analyses require billions of data points Example: Find correlations between 1000 wafer sort parameters and 1000 final test parameters over 1000 lots Engineers complain that their biggest problem is GETTING the data they need for analysis Relationships in data are complex Example: Using chip IDs to relate data across multiple operations Example: Correct interpretation of retests 20

21 Example: Correlation 21

22 Correlation Analysis The Old Way 22

23 Correlation Analysis Big Data 23

24 Big Data & Quality 24

25 The Need Shifting from Defects per Million to Defects per Billion 25

26 The Problem RMA & Failure Analysis Test Equipment 26% No Problem Found 32% Test Operation 4% Test Program 10% Fab Process 28% No Problem Found Fab Process Test Program Test Operation Test Equipment 26

27 The Challenge COST TIME BIG DATA EXPERTISE 27

28 Back to Basics 28

29 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 29

30 Escape Prevention ATE / TP The STDF PRR.NUM_TESTS field tells us the number of tests executed on the part. It should be relatively stable throughout the lot 30

31 Escape Prevention Test Ops Excessive probing when operation ignores probe mark spec for a device and keeps on probing to get the yield 31

32 Escape Prevention Test Program Human error is one of the main contributors to test escapes and RMA. Here the PE commented a few blocks in the TP for debug and forgot to uncomment before production release: SBL SBL drop of soft bin 11 from ~3% to 0 following new TP revision 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. 32

33 Escape Prevention Test Program ~95 Sigmas ~95 Sigmas Extremely loose test limits may mask real test performance problems 33

34 Quality Index 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 Quality Rule Inputs Wafer Geography Lot/Wafer Yield etc. Quality Index 34

35 Data Feed Forward 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 35

36 Data Feed Forward Drift 1. ECID Data 2. FT1 Measurements Tester Test Program running FT2 operation Database at subcon Real-time data! No test time impact! 36

37 No Problem Found Combinations of chips causing issues: IC1 IC2 IC3 PCB 37

38 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 38

39 Conclusion Big Data has arrived to semiconductor test Are YOU ready for the challenge? 39

40 Thank You! 40