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1© 2016 The MathWorks, Inc.
Data Analytics for Semiconductor Manufacturing
2
Analytics uses data to drive decision making, rather than gut feel or intuition. Analytics uses data
to derive insights which drives business actions and therefore produces business outcomes.
Analytics Maturity
Level of Sophistication
Prescriptive
Predictive
Diagnostic
Descriptive
What should
happen?
What will
happen?
Why did it
happen?
What
happened?Co
mp
eti
tive A
dvan
tag
e
What do we mean by “Data Analytics” ?
Copyright of Shell Global solutions
3
Data Analytics 3.0
• Structured Data set based Descriptive Analysis
• Decision making from Historical Data
• Big data (Structured + Unstructured)
• IoT (Internet of Things)
• 3Vs (Volume, Variety, Velocity) + Value
• Machine learning and Deep learning
• Optimized workflow and decision making with
prescriptive analytics
• In-Memory processing with streaming data
• Smart factory
• Enterprise system Integration
93.68%
2.44%
0.14%
0.03%
0.03%
0.00%
0.00%
0.00%
5.55%
92.60%
4.18%
0.23%
0.12%
0.00%
0.00%
0.00%
0.59%
4.03%
91.02%
7.49%
0.73%
0.11%
0.00%
0.00%
0.18%
0.73%
3.90%
87.86%
8.27%
0.82%
0.37%
0.00%
0.00%
0.15%
0.60%
3.78%
86.74%
9.64%
1.84%
0.00%
0.00%
0.00%
0.08%
0.39%
3.28%
85.37%
6.24%
0.00%
0.00%
0.00%
0.00%
0.06%
0.18%
2.41%
81.88%
0.00%
0.00%
0.06%
0.08%
0.16%
0.64%
1.64%
9.67%
100.00%
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Data Analytics for Semiconductor Manufacturing Process
Data is being logged everywhere
Data comes from many disparate sources
Get insight from aggregated data and
make actionable decisions to
– Improvement Yield
– Detecting error
– Reduction Manufacturing cost
– Process optimize
– Prognostics
– equipment health/Maintenance
Actionable
InsightWafer
Oxidation
Photo lithography
EtchingThin Film
Deposition
Metallization
EDS
5
Workflow of Analytics for Integrated Wafer Inspection system
Process, feature data
Business and Transactional
Data Aggregator / Reducer and
/Automated / Enterprise System
1. Big Data Access
Big/Diverse Data Sources
2. Analytics
Machine Learning
3. Integrate to
Enterprise System
Automation, Business
Data Exploration Analytics Modeling & Evaluation Integration
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“Any collection of data sets so large and complex that it
becomes difficult to process using … traditional data
processing applications.”(Wikipedia)
“Any collection of data sets so large that it becomes
difficult to process using traditional MATLAB functions,
which assume all of the data is in memory.”(MATLAB)
Various Data Sources from complex process
image, sensor, numeric, text, etc.
Rapid Data exploration from enormous number
of manufacturing equipments
Scalable Data processing with scalable HW
Identify the value from uncertainties
Statistics, Clustering, Classification, Regression
Ease of deployment
Support common development environment
(C/C++, .Net, Java, Excel, Hadoop)
Big Data
Volume
Variety
Velocity
Value
1. Big Data Access
Big/Diverse Data Sources
7
“Reading Big Data in MATLAB”
ComplexityEmbarrassingly
Parallel
Non-
Partitionable
MapReduce
Distributed Memory
SPMD and distributed arrays
Load,
Analyze,
Discard
parfor, datastore,
out-of-memory
in-memory
Researchers
“Techniques for Big Data in MATLAB”
1. Big Data Access
Big/Diverse Data Sources
8
Why use MATLAB for Machine Learning?
Complete environment for Machine Learning
(prototyping to production)
Verified and trusted machine learning algorithms
Classification Learner app that lets you perform
common machine learning tasks interactively
Tightly integrated with Signal Processing, Image
Processing, Computer Vision and other workflows.
Open and flexible architecture enables a customized
workflow
2. Analytics
Machine Learning
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Support Vectors
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0
0.5
1
1.5
x1
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group1
group2
group3
group4
group5
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ChallengeApply machine learning techniques to improve overlay
metrology in semiconductor manufacturing
SolutionUse MATLAB to create and train a neural network that
predicts overlay metrology from alignment metrology
Results Industry leadership established
Potential manufacturing improvements identified
Maintenance overhead minimized
ASML Develops Virtual Metrology
Technology for Semiconductor
Manufacturing with Machine Learning
Link to user story
“As a process engineer I had no
experience with neural networks or
machine learning. I worked through
the MATLAB examples to find the
best machine learning functions for
generating virtual metrology. I
couldn’t have done this in C or
Python—it would’ve taken too long
to find, validate, and integrate the
right packages.”
Emil Schmitt-Weaver
ASML
Cutaway of a TWINSCAN and Track as wafers receive
alignment and overlay metrology
2. Analytics
Machine Learning
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Machine Learning Workflow
MODELSUPERVISED
LEARNING
UNSUPERVISED
LEARNING
KMEANSAUTO-
ENCODER
PCAGMM CLASSIFICATION
REGRESSION
LOAD
DATA
PREDICTIONMODELNEW
DATA
Step 1: Training
Step 2: Evaluation
PREPROCESSING TRAINING
2. Analytics
Machine Learning
Iterate
11
Overview of Machine Learning in MATLAB
Clustering
K-means.
Fuzzy K-meansHierarchical
Neural Network Gaussian
Mixture
Hidden Markov Model
Regression
Decision
Trees
Ensemble
MethodsNeural Network
Non-linear Reg.
(GLM, Logistic)
Linear
Regression
Classification
K-means.
Fuzzy K-meansHierarchical
Naive Bayes, Nearest Neighbors
Gaussian
Mixture
Hidden Markov Model
Unsupervised
Learning
Supervised
Learning
2. Analytics
Machine Learning
12
Integrate Analytics Application to Enterprise System 3. Integrate to
Enterprise System
Automation, Business
Separated Storage;Local DriveNetwork DriveDatabase
Typical Workflow
Process Control System/Reporting System
Re-coding
Increase
Decrease
Time consumptionOperation overhead
Error
Pitfall…Performance
Yield rate
Efficiency…
IT
- IT Mgr.
- Enterprise
Architect
- Programmers
Business Units
- Head of BU
- Domain experts
- (Data scientists)
Business Units
- Head of BU
- Domain experts
- (Data scientists)
Business Units
Analytics Group
- Head of BU
- Head of Analytics
- Domain experts
- Data scientists
13
Deployment for Analytics Integration 3. Integrate to
Enterprise System
Automation, Business
MATLAB
MATLAB
Compiler SDK
C/C++ExcelAdd-in JavaHadoop .NET
MATLAB
Compiler
MATLABProduction
Server
StandaloneApplication
Integrated into production IT
environments without recoding or
creating custom infrastructure
Packaged as compatible components
with a wide range of systems (Java,
.NET, Excel, Python & C/C++)
Shared as standalone applications or
run from: web, database servers,
desktop, enterprise applications, etc.
14
Solutions for Web/Enterprise Application ServersMATLAB Production Server
3. Integrate to
Enterprise System
Automation, Business
Most efficient path for creating
enterprise applications
Deploy MATLAB programs into pr
oduction
• Manage multiple MATLAB programs
and versions
• Update programs without server resta
rts
• Reliably service large numbers of con
current requests
Integrate with web, database, and
application servers
15
Integrate Analytics Application to Enterprise System 3. Integrate to
Enterprise System
Automation, Business
Suggested Workflow
MATLAB Production Server
Request
Broker
Enterprise System/Process Automation
AnalyticsIntegration
Without Re-coding
Data from manufacturing process- Wafer- Oxidation- Photo Lithography- Etching- Overlay Metrology- Scanner / SEM image- Fab/Probe/Assembly/Test Yields- Economic market data- etc
Results/ Reporting system(C# and Java)
- Process Optimization- Yield Improvement- Rapid processing
- Increase efficiency- IP management
Out of Memory Data Access for Massive Data(Hadoop, DB)
Test deployment in R&D part
C/C++ Java .NET
ExcelAdd-in
HadoopStandaloneApplication
Prototyping Analytics solution
16
Summary :
MATLAB Data analytics in Semiconductor Industry
1. Big Data Access
Big/Diverse Data Sources
3. Integrate to
Enterprise System
Automation, Business
2. Analytics
Machine Learning
Scale Up Computation Analytics / A.I System IntegrationData Exploration/ Visualization
• Parallel Computing toolbox• MATLAB Distributed
Computing Server
Multicore DesktopCluster / Cloud ComputingGPU computingTask Parallel / Data Parallel
Big data Access Datastore, MapReduceHDFS supportDB, No SQL support
• MATLAB• Database toolbox
• Statistics and Machine learning
• (Global)Optimization • Neural Network toolbox
• MATLAB Compiler• MATLAB compiler SDK• MATLAB Production Server
Machine Learning/ Deep LearningConvolutional Neural NetworkArtificial Neural NetworkLinear/Non-Linear Regression …
C/C++ Java .NET
ExcelAdd-in
HadoopStandaloneApplication
Spark
Stand-alone ApplicationMulti-language target libraryHadoop integrationWeb service
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