big data analytics for semiconductor mfg5...1) ibm’s big data platform and 2) custom applications...

© 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


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


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


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


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


Actionable insight

Reporting & interactive analysis

Data types

Transaction andapplication data



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


Actionable insight


Deep analytics & modeling

Data types


Predictive analytics

and modeling


Operational systems

Archive

Enterprise Warehouse

Staging area

Transformation to target architecture – stage1Provide dedicated analytics processing for faster, deeper analysis and modeling


Actionable insight

Exploration and landing

Trusted data



Data types


Enterprise content

Social data

Image and video

Third-party data


and modeling

Reporting, analysis, content

analytics


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


Actionable insight

Exploration, landing and

archive

Trusted data



Data types


Enterprise content

Social data

Image and video

Third-party data


and modeling


analytics


Operational systems

Archive

Transformation to target architecture – stage3Leverage Hadoop for “queryable” archive


Actionable insight


archive

Trusted data



Data types Real-time processing & analytics


Machine andsensor data

Enterprise content

Social data

Image and video

Third-party data

Decision management


and modeling


analytics


Operational systems

Transformation to target architecture – stage4Leverage data in motion and streamline processing of extreme volumes



Actionable insight


archive

Trusted data



Data types Real-time processing & analytics



Enterprise content

Social data

Image and video

Third-party data

Decision management


and modeling


analytics


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


Watson FoundationsWatson Foundations

Information Integration & GovernanceINFORMATION SERVER, MDM, G2, GUARDIUM, OPTIM


archive

Trusted data



Data types Real-time processing & analyticsSTREAMS, DATA REPLICATION



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


and modeling


analytics


SPSS MODELER

COGNOS BICOGNOS TM1

DATA EXPLORERSPSS ANALYTIC

CATALYST

SPSS MODELER GOLD

IBM Big Data & Analytics Offerings


� 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


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


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”


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


From IBM presentation at SemiKorea, Feb2014

Use Case 2: Adaptive Testing that enables global visibility and decision-making with Big Data


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


Use Case 3: Visualization of Sensor data with scoring algorithms and full drill-down capability


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


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


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


Big Data and Analytics to Cognitive Computing


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?


Why did it happen?


What could happen?


Deep Analytics data zone What did

I learn, what’s best?

Cognitive

big data analytics for semiconductor mfg5...1) ibm’s big data platform and 2) custom applications...

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