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© 2014 IBM Corporation
Big Data & Analytics for Semiconductor Manufacturing
半導体生産におけるビッグデータ活用半導体生産におけるビッグデータ活用半導体生産におけるビッグデータ活用半導体生産におけるビッグデータ活用
Ryuichiro Hattori 服部 隆一郎
Intelligent SCM and MFG solution Leader
Global CoC (Center of Competence) Electronics teamGeneral Business Services
IBM
© 2014 IBM Corporation
Agenda
� What is Big Data ?
� Big Data in Semiconductor Manufacturing
� Big Data and Analytics architecture
� Big Data Analytics use case in IBM Microelectronics
� Summary
© 2014 IBM Corporation3
Volume Velocity Veracity*Variety
Data at rest
Terabytes to exabytes of existing
data to process
Data in motion
Streaming data, milliseconds to
seconds to respond
Data in many forms
Structured, unstructured, text,
multimedia
Data in doubt
Uncertainty due to data inconsistency& incompleteness,
ambiguities, latency, deception, model approximations
What is Big Data ? - Big data is about All Data
© 2014 IBM Corporation
Fall 2013
Problem statement:
“Conventional or standard analytical methods and technologies are built for predictive
modeling on a small scale, not for investigation of hundreds or thousands of potential
factors and interactions”
“Engineers with standard analytical techniques and tools have become the bottleneck,
outpaced by data volumes and complexity”
“New methods and software are needed to bridge the gap between analysis and action”
“Automated data mining and analysis tools are needed to explore and uncover problems
and opportunities that lead to action and potential manufacturing operation improvements
that differentiate one company from its competition”
Big Data in Semiconductor Manufacturing
© 2014 IBM Corporation
Big DataBig DataBig DataBig Data HadoopHadoopHadoopHadoop
≠
“There’s a belief that if you want big data, you need to go out and buy Hadoop
and then you’re pretty much set. People shouldn’t get ideas about turning off
their relational systems and replacing them with Hadoop…
As we start thinking about big data from the perspective of business needs,
we’re realizing that Hadoop isn’t always the best tool for everything we need to
do, and that using the wrong tool can sometimes be painful.”
Ken RudinHead of Analytics at Facebook
© 2014 IBM Corporation
IBM PoV on Big Data and Analytics architecture
Information Integration & Governance
Systems Security
On premise, Cloud, As a service
Storage
New/Enhanced
ApplicationsAll Data
What action should I take?
Decision management
Landing, Exploration and Archive data zone
EDW and data mart zone
Operational data zone
Real-time Data Processing & Analytics What is happening?
Discovery and exploration
Why did it happen?
Reporting and analysis
What could happen?
Predictive analytics and modeling
Deep Analytics data zone What did
I learn, what’s best?
Cognitive
© 2014 IBM Corporation
Actionable insight
Reporting & interactive analysis
Data types
Transaction andapplication data
Predictive analytics and modeling
Reporting and analysis
Operational systems
Archive
Enterprise Warehouse
Staging area
Transformation to target architecture - startLeverage column-store and in-memory capabilities to improve performance and enable reporting & analysis directly against operational data
© 2014 IBM Corporation
Actionable insight
Reporting & interactive analysis
Deep analytics & modeling
Data types
Transaction andapplication data
Predictive analytics
and modeling
Reporting and analysis
Operational systems
Archive
Enterprise Warehouse
Staging area
Transformation to target architecture – stage1Provide dedicated analytics processing for faster, deeper analysis and modeling
© 2014 IBM Corporation
Actionable insight
Exploration and landing
Trusted data
Reporting & interactive analysis
Deep analytics & modeling
Data types
Transaction andapplication data
Enterprise content
Social data
Image and video
Third-party data
Predictive analytics
and modeling
Reporting, analysis, content
analytics
Discovery and exploration
Operational systems
Archive
Transformation to target architecture – stage2Leverage Hadoop to capture operational data, leverage additional data types and enable exploration of data prior to normalization
© 2014 IBM Corporation
Actionable insight
Exploration, landing and
archive
Trusted data
Reporting & interactive analysis
Deep analytics & modeling
Data types
Transaction andapplication data
Enterprise content
Social data
Image and video
Third-party data
Predictive analytics
and modeling
Reporting, analysis, content
analytics
Discovery and exploration
Operational systems
Archive
Transformation to target architecture – stage3Leverage Hadoop for “queryable” archive
© 2014 IBM Corporation
Actionable insight
Exploration, landing and
archive
Trusted data
Reporting & interactive analysis
Deep analytics & modeling
Data types Real-time processing & analytics
Transaction andapplication data
Machine andsensor data
Enterprise content
Social data
Image and video
Third-party data
Decision management
Predictive analytics
and modeling
Reporting, analysis, content
analytics
Discovery and exploration
Operational systems
Transformation to target architecture – stage4Leverage data in motion and streamline processing of extreme volumes
© 2014 IBM Corporation
Information Integration & Governance
Actionable insight
Exploration, landing and
archive
Trusted data
Reporting & interactive analysis
Deep analytics & modeling
Data types Real-time processing & analytics
Transaction andapplication data
Machine andsensor data
Enterprise content
Social data
Image and video
Third-party data
Decision management
Predictive analytics
and modeling
Reporting, analysis, content
analytics
Discovery and exploration
Operational systems
Information Integration
Data Matching & MDM
Security & Privacy
Lifecycle Management
Metadata & Lineage
Transformation to target architecture – stage5Extend transformation, matching, security and governance capabilities to ALL data
© 2014 IBM Corporation
Watson FoundationsWatson Foundations
Information Integration & GovernanceINFORMATION SERVER, MDM, G2, GUARDIUM, OPTIM
Exploration, landing and
archive
Trusted data
Reporting & interactive analysis
Deep analytics & modeling
Data types Real-time processing & analyticsSTREAMS, DATA REPLICATION
Transaction andapplication data
Machine andsensor data
Enterprise content
Social data
Image and video
Third-party data
Operational systems
BIGINSIGHTS
PUREDATAHADOOP
DB2, INFORMIX
PUREDATA TRANSACTIONS
PUREDATA ANALYTICS
DB2 BLUPUREDATA ANALYTICS
DB2 WAREHOUSE
PUREDATA OPERATIONAL ANALYTICS
Actionable insight
Decision management
Predictive analytics
and modeling
Reporting, analysis, content
analytics
Discovery and exploration
SPSS MODELER
COGNOS BICOGNOS TM1
DATA EXPLORERSPSS ANALYTIC
CATALYST
SPSS MODELER GOLD
IBM Big Data & Analytics Offerings
© 2014 IBM Corporation
� Leverages all data available in fab: logistics, metrology, inspection, test, tool sensors
Combination of :
1) IBM’s Big Data platform and 2) custom applications
largely developed, built and driven by IBM Research expertise
Equipment Sensor Data Yield analysis routines
Identifies variables and provides prediction~10 Billion data points per day
Big Data Analytics approach in IBM Microelectronics
© 2014 IBM Corporation
Demand/SupplyPlanning
Manufacturing Execution System(MES)
Sensor
Systems
Equipment
Control
Process, Measurement and
Test Equipment Communications
AMHS
Control
Automated Material HandlingAutomated Reticle Handling
Advanced Process
Controls
Information Warehouse
&E-biz interface
Product
DemandManagement
Equipment
MaintenanceAnd
Scheduling
RecipeMgt
Energy
Management
Part Number
Build
Product Dispatch
EngineeringAnalysis
Tools
Enterprise
Factory
Adaptive Test
Engine
Several real use cases are described on following pages
Big Data Analytics use case in IBM Microelectronics
© 2014 IBM Corporation
Use Case 1: Big Data approach to the problem of large dataset analysisTraditional
TesterData Warehouse
Largedatasetretrieval
Largeanalysisroutine
Reviewreports
Tester
InfoSphere Streams
Interactivereview
Near real-time analysis
Model results in-memory
New approach
� Challenge: Existing analysis methods struggle with current data volumes� pulling and manipulating data takes too long� thousands of charts and graphs that require manual review� analysis may not be complete before product is shipped
“In-flight Analytics”
© 2014 IBM Corporation
Partial Least Squares (PLS) model compares actual yield to previous results� analysis output highlights what has changed
Not enough ‘All Goods’
Too many ‘Partial Goods’
Automated Streams solution:
• compares yield by test pattern to historical data
• identifies unusual yield behavior, based on multivariate model
• larger bars indicate larger deviation from historical yield
• has been used to immediately identify problems on leading edge of new production
• problem identified before the first wafer had completed testing
• new data added to existing model and kept in memory for fast and easy analysis
Yield ContributionBy Pattern
Benefits:
• 20% reduction in engineering labor• first quality escape prevented - $650k in avoided warranty expense
Use Case 1: Real-time multivariate analysis of wafer test patterns with Streams
© 2014 IBM Corporation
From IBM presentation at SemiKorea, Feb2014
Use Case 2: Adaptive Testing that enables global visibility and decision-making with Big Data
© 2014 IBM Corporation
What we did:
� Collected and enabled quick review of massive amounts of sensor data, in a simple dashboard� Identified tool issues and parameters that influence critical product measurements� Developed scoring algorithms, including advanced info theory to highlight relationships
� ease of use, guides analyst to significant findings� Fully automated, with linked reports for full drill-down capability
Benefits:
� Documented savings > $13M during first two years of use� Drives actions for tool stability and control, process centering, yield learning, scrap avoidance� Systematic implementation has continued throughout the fab
Challenge:
� Yield learning is the most direct contributor to fab profitability and time to market� Huge volume of data (billions of points per day) with many subtle interpretations� Want to maximize usefulness of semi-structured tool sensor data for variety of problem solving� Large engineering team, with varying skills in analysis, statistics, data mining
Use Case 3: Usage of Sensor data in IBM fab for yield control and asset optimization
© 2014 IBM Corporation
Use Case 3: Visualization of Sensor data with scoring algorithms and full drill-down capability
© 2014 IBM Corporation
ChallengeChallenge SolutionSolution
Quality and supply chain managers need advanced techniques to examine quality date from tens of thousands of parts (incoming, manufactured, deployed) and to provide better, more proactive quality management
Software system which uses proprietary IBM technology to detect & prioritize quality problems earlier with fewer false alarms, coupled with push alert functionality for IBM & suppliers to proactively detect & manage quality issues at any stage of product lifecycle
Key Innovations
�Earlier identification of quality issues through proprietary analytic techniques
�Fewer false alarms
�Structured issue prioritization, management, follow-up
Distills an ocean of supply chain quality data into prioritized, actionable issues
Business Value at IBMBusiness Value at IBM
� Cost savings – $39M in hard warranty savings, with additional soft savings and benefits in other areas
� Proactive quality mgt – identify and resolve issues before they become problems, up to 6 weeks earlier than traditional SPC
� Improved quality processes – improves quality process efficiency & effectiveness
Results from QEWS Proof of Concept at external client
Use Case 4: Quality Early Warning System (QEWS) to identify trends in Supply Chain before traditional SPC
© 2014 IBM Corporation
GPS
Semiconductor firms see significant opportunities for Big Data to optimize the way they execute across functions
Product
Development &
Manufacturing… compress design, development & manufacturing lead time and improve yield and asset utilization
Marketing & Sales... design and execute more effective marketing with optimized product assortments, affinities and pricing
Supply Chain & Distribution... optimize inventory and assets and deliver a reduction in supply chain and distribution costs with single view product
Market Research &
Product Ideation... align product concepts with
consumer desires, improve new product ideas, and new product
launch effectiveness for IoT
Procurement &
Vendor
Management... embed insight into business processes from Manufacturer to Distributor to Customer to Consumer
Finance...grow revenue and improve margins with greater business performance insight, and improved forecasting and planning
External Data
Massive Internal Data
Field and Warranty
Management... collect field data from connected devices, understand part behavior, predict failures, reduce warranty cost
© 2014 IBM Corporation
Invest in a Invest in a big data & big data & analytics analytics platformplatform
Be confident Be confident with privacy, with privacy, security and security and governancegovernance
Imagine It. Realize It. Trust It.
Build a culture Build a culture that infuses that infuses
analytics analytics everywhereeverywhere
SummaryThree Key Imperatives for Big Data & Analytics Success
Focus on business needsApply how well use data
© 2014 IBM Corporation
Big Data and Analytics to Cognitive Computing
Information Integration & Governance
Systems Security
On premise, Cloud, As a service
Storage
New/Enhanced
ApplicationsAll Data
What action should I take?
Decision management
Landing, Exploration and Archive data zone
EDW and data mart zone
Operational data zone
Real-time Data Processing & Analytics What is happening?
Discovery and exploration
Why did it happen?
Reporting and analysis
What could happen?
Predictive analytics and modeling
Deep Analytics data zone What did
I learn, what’s best?
Cognitive
© 2014 IBM Corporation
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