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Early Detection Solution Improved Profitability Across Global Test Operations

Early Detection Solution Improved Profitability Across Global Test Operations. Debbora Ahlgren VP Sales & Marketing OptimalTest 26-May-2009. Business Model Transitions. Process Node (Year). 90nm (2003). 65nm (2006). 45nm (2008). 32nm (2010). 22nm (2012). Window Of Opportunity.

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Early Detection Solution Improved Profitability Across Global Test Operations

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  1. Early Detection SolutionImproved Profitability Across Global Test Operations Debbora Ahlgren VP Sales & Marketing OptimalTest 26-May-2009

  2. Business Model Transitions Process Node (Year) 90nm (2003) 65nm (2006) 45nm (2008) 32nm (2010) 22nm (2012) Window Of Opportunity Window of Opportunity With the move to 300mm- diameter wafers, the price tag for an advanced production fab has become out of reach for all but the largest IDMs…* TI Source: Gartner, 2008 Source: Gartner, 2008

  3. Yield Loss at 65nm and Beyond Source: Broadcom, 2008 Systematic variations are contributing to >60% yield loss at the 65nm as Compared to <5% at the 350nm node.

  4. Integrating the DistributedManufacturing Model Foundry Services

  5. Cost of Test – ITRS Focus Goal is to Optimize Product Cost Balance of cost/value of Design, Manufacture, Yield Learning & Test

  6. ITRS TWG 2009 Plans • DFT alignment with design TWG • Socket performance vs. frequency • Expansion of specialty devices • Adaptive Test • SiP & 3D silicon Test Suggest that the ITRS is cutting short the potential. The real potential is to work for transparency and interoperability across the integrated fabless manufacturing model.

  7. Early Detection Solution Robust IT Infrastructure • Promotes transparency across distributed value & supply chain • Provides for the implementation of rules (through expert rules engine) that transcends lots, device families and enterprise boundaries • Integrates Yield Learning, Quality and Reliability functions • NPI • Outlier Detection • Advanced Adaptive Test for Yield, Quality and TTR

  8. OptimalTest Value Propositions OptimalTest delivers solutions for optimized test operations through 4 levels of software capabilities: Real-Time Control – of Test Cell Execution Station Controller (OT-Box) applicationTest Cell control in real-time: Yield Learning & Reclamation, Test Operations Efficiency (TTR, Test Cell degradation) Product Quality (outlier detection), and Quality Real-Time Monitoring – of a fleet of Test Cells for Optimized EfficiencyControl Room applicationReal-time fleet monitoring: Yield degradation prevention, Operational Efficiency & Immediate Quality Attention Near-Time Detection – of Product & Test Operational IssuesEarly Detection Solution & OT-DashboardProduct quality & performance monitoring & fleet monitoring in near-time: Yield degradation prevention & Yield Reclamation, Operational Efficiency and Quality Off-Line Analysis & simulation – of Test Operations: Products, Fleet, ProcessesReporting, Analysis & Simulation applications and OT-DashboardProduct performance analysis & off-line simulation: Yield, Efficiency (TTR), and Quality (Outlier Detection)

  9. What is the value of Early Detection? • Test results are situation specific • Specific device and specific test cell • “Bad” and “Good” are not absolute measures • Near Real-Time (or “Near-Time”) Post Processing using robust “expert” rules improves operational efficiency for test • Re-evaluate specific device results vsother devices (historical and across multiple test cells) • Such Operational Efficiency requires the establishment of a Baseline of Fleet, Product and Processes • This requires a database and an expert rules engine – OT-Rules • Only Early Detection supports the “Near-Time” evaluation & comparison of results across an entire fleet of testers • Across multiple enterprises (SATs and distributed test floors) • Across lots from multiple foundries

  10. Outlier Detection for Product Reliability OUTLIER MANAGEMENT CAPABILITIES THROUGH STATE-OF-THE-ART ADAPTIVE TESTING

  11. Advanced Adaptive Test for TTR,Quality Control & Yield Learning • Location of Baseline Die • selected according to • various algorithms: • Next to E-test structures (for maximized correlation between test sockets) • Spread-out equally in each of the 3 ring areas (for maximized area coverage) • In areas of different yield signatures • In most of the lithography exposure locations • In areas corresponding with Fab defect sampled areas

  12. Early Detection Solution - Implementation • Data logs from any origin & any format are transferred to the OT database at the end of each Run / Pass / Execution • An OptimalTest Station Controller (OT-Box) connected to any test cell; or • A Proxy (OT-Proxy) on any tester; orOT-Proxy is a light piece of SW (a “deamon”) installed on a tester that communicates directly to the OT-Database • Any data log format from any family / model of tester • (i.eSTDF - heavy or light, comprehensive or summary) • Re-evaluation of the data logs is executed against 2 types of expert rules • Product level rule: Executed whenever new datalog files enter the data base • Cross-Entity rule: Executed on a defined periodic time basis • Once per shift, once per day, etc. • Once an issue is detected based on the defined rules • An eMailis sent to the responsible personnel with a description of the problem and a link to a specific report (OT-Reports/OT-Dashboard) illustrating the issue; and • An alert is sent to the responsible personnel; and • A disposition action can be executed

  13. Without Optimal Test at the SAT Parsing of non-OTDF is more processor-intensive

  14. With OT Boxes and Using OTDF Each OT customer has their own OT Database

  15. With OT Proxy and Using OTDF Each OT customer has their own OT Database

  16. Early Detection Solution -- Architecture Customer Defined Rules eMailNotification Product Rule (End of Wafer or Lot) Cross- Entity Rule (Customer-Defined Period) With attached report OTDF (++) Feedback OTDF Rule Feedback Scheduled Analysis Feedback Any Logs (STDF or Other) OptimalTest DB Proxy OT-Dashboard Any Other Testers OT- DispoAutomatic Disposition (e.g. hold/release lot)

  17. Data Integrity QCT/OT Confidential

  18. OT-Rules Generated eMail Alert Equipment Outlier Alert – Product exhibiting different yield on specific tester in a fleet or across enterprises “Manufacturer A” SAT1_33 M220

  19. OT-Rules Generated eMail Alert (Example #2) (A rule alerting for a site yield degradation issue (>5%)

  20. OT-Reports – Illustrating the Problem in the eMail Alert (A rule alerting for a site degradation issue (>5%)

  21. OT-Dashboard Examples – Product Engineer's View Wide variety of "widgets" with all relevant KPI's Multiple layouts accessible via tabs Highlight outliers

  22. OT-Dashboard Examples – Throughput Focus Rule Efficiency Magnify yield fluctuations Outlier Equipment Alarms

  23. OT-Dashboard Early Detection Solution – Case Study Example 1 4 7 2 5 8 Legend 1 – Device A Final Yield 2 – Device A 1st Pass Yield 3 – Device A Total Units 4 –Device B Final Yield 5 – Device B 1st Pass Yield 6 – Device B Total Units 7 – Device B Retest Rate (Facility 1 – OT-Boxes enabled) isn’t crossing 5% threshold) 8 – Device B Final Yield by Tester (Facility 1 “fleet” is stable) Legend 1 – Device A Final Yield 2 –Device A 1stPass Yield 3 – Device A Total Units 4 – Device B SC2X 1st Pass Yield 6 – Device B Total Units 7 –Device B Retest Rate (Facility 1 – with OT-Boxes -- isn’t crossing 5% threshold) 8 – Device B Final Yield by Tester (Facility 1 “fleet” is stable) 3 6

  24. Benefits of Early Detection Solution • Results Re-Evaluation  control, quality & health of all test operations • Identify issues in: Yield, Efficiency, Productivity, Quality & Data-Integrity • Detection of Outlier Equipment all test assets (ATE, peripherals, consumables) • Proactive detection of trending and marginal equipment before they become outliers & before equipment failure (Fleet baseline) • Detection of Product & Quality issues  improved product quality and reduced field returns • Proactive detection of Test Program instabilities & marginalities, Bin switching, Recoverability, Re-Tests, 1stpass yields, QA, Correlation / Golden-units pass rate, run-rate & performance issues • Detection of Data Integrity issues  consistent results and reliable decision makingProactive detection of Wafer Maporientation issues, Holes, STDF vs wafer maps vsSummary files vs shipping maps etc • Detection of Operational Issues improved overall operational efficiency (OEE) • Proactive detection of failure to follow operational processes and procedures. Pauses, Set-ups, Re-tests • End-of-Line gate-keeping  improved overall operational efficiency (OEE) • Proactive verification that material was processed based upon the intended criteria (Flow, Disposition actions, yield, Retest etc) • OT-Post Engine offers: • Device Outlier Detection PAT, S-PAT, D-PAT, ULPY, NNR and other advanced outlier detection techniques • Bin Re-Classification Better categorization of devices for device parameters including Speed, Voltage Parameter “matching” for optimized Multi-Chip-Package or System-in-Package performance

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