The Importance of Traceability:

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1 The Importance of Traceability: Connecting the Dots to Optimize the Electronics Supply Chain Thomas Sonderman VP & GM, Software Business Unit

2 Understanding Supply Chain Yield Dynamics Requires Complex Enterprise Level Supply Chain Optimization: Big Data Digital Threads Package to Tool Correlations Real-time Dashboards Best in Class Yield Analytics Structural Failures Component Matching Package Interconnection Delays Bump Coplanarity Device Parametric Variation Performance Failures Process Tool Failure Solutions Must Remove Risk from the Electronics Supply Chain Device Contamination 2

3 Electronics Supply Chain Optimization Analytics Across the Value Chain Deep into the process Centralized db s Fab 1 Fab 2 Adv. Package Multi-chip Product Fab Level Process Area Tool Level Enterprise YMS Sophisticated Yield Management System Supplier Chain Traceability (Digital Threading) Dashboards for Complete Visibility from Technician to CEO Chamber Trace Data Bi-Directional Communication Enterprise FDC Fault Detection with Wafer/Die Level Correlations Big Data Rudolph Confidential 3

4 Solutions MUST Enable Smart Manufacturing The Drive Towards Increased Productivity The evolution toward smart manufacturing is enabled by the confluence of Connectivity Computing Power Intelligent Software Ability to Manage MASSIVE DATA Electronics Suppliers Who Act Decisively will Benefit by Creating Added Value for their Customers 4

5 Big Data Defined

6 Why Can t Decisions Be Made Quicker? 6

7 New Paradigms are Required

8 Smart Manufacturing Platform 8

9 Advanced Predictive Analytics Enterprise YMS Enterprise FDC Big Data Based Predictive Analytics Platform Proven Solutions HVM Ready, IOT Enabled TYPICAL FABWIDE FDC DATA VOLUMES Daily Rec. Events : 16 Mil Daily Statistics generated: ~32 Mil Daily Samples : 1.1 Billion Data Retention : ~ Billion 20TB+ Database (Must be highly transactional) High speed data support down to 1ms of data Tool FDC Tool FDC Tool FDC Scalable Architecture Apps servers to support 100 s of tools per fab TelNEXX Stratus TelNEXX Stratus TelNEXX Stratus TelNEXX Stratus APOLLO APOLLO APOLLO APOLLO MOCVD MOCVD MOCVD MOCVD Connection to over 1000 Tools in Value Chain ~600+ signals per tool 9

10 Predictive Analytics the C in FDC Comprehensive Algorithm Suite PCA ANOVA Univariate Validation PLS Supervised Visual Dynamic Time Warping UVA / MVA Pre-filter Cluster Un-Supervised Prioritization MLR Supervised Quantification Adaptive Decision Trees Unsupervised Visual TYPES OF MACHINE LEARNING Supervised Prioritization PCA Identify Clusters What Defect Excursions? Decision Trees Prioritize Results Which Signal to Adjust? MLR Quantify Results How Much to Adjust? 10

11 Reports Intelligent Analysis Engines E-Test Tools Wafer-Centric Defect Tools Data Streams Metrology Tools Control Tools Control Tool-Centric Process Tools Big Data Platform Design info Defect data Metrology Sort Yield, data Parametric Data Warehouse MES & Equip data Parametric Test data Analytics runs in background constantly Models Database Comparator Predictor Learner Advisory Out Tool Advisory In Tool Anomaly Detection Prediction Root Cause Analysis Teach

12 Intelligent Digital Threading PERFORMANCE YIELD FAB 1 B FAB 2 AP ACTIONABLE DATA, NOW! 12

13 FAB 2 ANALYTICS Viz Yield Spec PROCESS STEP VM YMS YM SPR PRODUCT THREAD SYNC ENGINE Limit Eq History Target Spec Metrology FDC Defect EQUIP Runtime Recipe METROLOGY ACTIONABLE DATA, NOW! Sensor 3D With Die Die Wafer Lot Batch Images F.T X-Ray T.F. 13

14 Advanced RF Device Digital Threading Duplexer Example 14

15 Advanced RF Device Digital Threading Duplexer Example 15

16 Process Analytical Complexity Advanced Dicer Example Multiple data types to be aligned Distance-based values Time-based values Raw Data Laser Grooving Laser 2 nd pass? Inspection (Groove) Step-cut Spindle 2 Step-cut Spindle 1 Inspection (Step Cut) Inspection IR Normalized, Aligned Data Laser Grooving Laser 2 nd pass? Inspection (Groove) Step-cut Spindle 2 Step-cut Spindle 1 Inspection (Step Cut) Inspection IR Analytical space 1 hour long runs ~230 signals per FDC run Normalization to lowest sampling rate Database Connectivity 16

17 Thread Synchronization Engine Data Flow FDC Config. Trace Establish sampling rate FDC Client Select a Wafer Select Signal Define the trim Predictive Analytics Client Perform Analytics Select trace to control data size Which runs FDC Wafer Run FDC Server Stores RAW FDC Data YMS Server Stores the Metrology Data Thread Synchronization Engine Alignment and Trim Engine Receives config. settings Pull data on the server side Performs the alignment Performs Global Trim Creates Extract DB / Extract Stats Calculations Predictive Analytics Server Stores the trimmed, aligned data Other Fab Data Performs Selected Trim* in memory Potential alignment Inspection Wafer Run Automate this process at the end of a wafer run 17

18 Thread Synchronization Engine 18

19 Intelligent Digital Signatures What does the Analysis Look Like? Run Start Run End SVID #1 SVID # Signal AVG Chips > 20um SVID #3 SVID # Signal AVG Chips < 20um SVID #5 SVID #6 Chip Depth categorization vs Trace signal average at cut time 19

20 Summary The Goal is Actionable Intelligence New Analytical Paradigms will be Required Intelligent Data Threading is Essential Across the Enterprise Deep into the Process Visible, Relevant, Secure and High Integrity Data is a Must 20

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