Table Of ContentSpringerBriefs in Applied Sciences and Technology
Tin-Chih Toly Chen
Production Planning and
Control in Semiconductor
Manufacturing
Big Data Analytics and
Industry 4.0 Applications
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Tin-Chih Toly Chen
Production Planning
and Control
in Semiconductor
Manufacturing
Big Data Analytics and Industry 4.0
Applications
123
Tin-Chih TolyChen
Department ofIndustrial Engineering
andManagement
NationalYangMingChiaoTungUniversity
Hsinchu, Taiwan
ISSN 2191-530X ISSN 2191-5318 (electronic)
SpringerBriefs inApplied SciencesandTechnology
ISBN978-3-031-14064-8 ISBN978-3-031-14065-5 (eBook)
https://doi.org/10.1007/978-3-031-14065-5
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Contents
1 Big Data Analytics for Semiconductor Manufacturing . . . . . . . . . . . 1
1.1 Big Data and Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Big Data Analytics for Manufacturing . . . . . . . . . . . . . . . . . . . . . 2
1.3 Classification of Big Data Analytics Techniques and Tools. . . . . . 5
1.4 Big Data Analytics for Semiconductor Manufacturing. . . . . . . . . . 8
1.5 Assessing the Benefits of Big Data Analytics
for Semiconductor Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . 13
1.6 Problems with Existing Methods . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7 Organization of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Industry 4.0 for Semiconductor Manufacturing . . . . . . . . . . . . . . . . 21
2.1 Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Industry 4.0 for Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.1 Cyber-Physical Systems. . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.2 Digital Twins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.3 Internet of Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.2.4 Cloud and Ubiquitous Manufacturing . . . . . . . . . . . . . . . . 31
2.3 Industry 4.0 for Semiconductor Manufacturing. . . . . . . . . . . . . . . 32
2.3.1 Cyber-Physical System Applications. . . . . . . . . . . . . . . . . 33
2.3.2 Internet of Things Applications . . . . . . . . . . . . . . . . . . . . 36
2.3.3 Problems with Existing Methods . . . . . . . . . . . . . . . . . . . 37
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3 Cycle Time Prediction and Output Projection . . . . . . . . . . . . . . . . . 41
3.1 Cycle Time Prediction in Semiconductor Manufacturing. . . . . . . . 41
3.2 Industry 4.0 and Big Data Analytics for Cycle Time
Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3 Cycle Time Prediction Methods. . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.1 Production Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.2 Principal Component Regression . . . . . . . . . . . . . . . . . . . 45
3.3.3 Parallel Radial Basis Function Networks. . . . . . . . . . . . . . 50
3.3.4 Classifying Feedforward Neural Networks. . . . . . . . . . . . . 53
v
vi Contents
3.3.5 Fuzzy C-Means-Back Propagation Network Ensemble. . . . 56
3.3.6 Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4 Defect Pattern Analysis, Yield Learning Modeling, and Yield
Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1 Defect Pattern Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 k-means Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 Parallel k-means Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4 Applications of Other Machine Learning and Artificial
Intelligence Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.5 Yield Learning Modeling and Yield Forecasting. . . . . . . . . . . . . . 72
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5 Job Sequencing and Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.1 Job Sequencing and Scheduling in Semiconductor
Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.2 Industry 4.0 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.2.1 Cyber-Physical System and Internet of Things
Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.2.2 Radio Frequency Identification and Internet
of Machines Applications. . . . . . . . . . . . . . . . . . . . . . . . . 81
5.3 Big Data Analytics Applications . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.3.1 Dispatching Rules Based on Big Data Analytics . . . . . . . . 83
5.3.2 Optimization Using Big Data Analytics . . . . . . . . . . . . . . 87
5.3.3 Multiple-Criteria Job Dispatching Based on Big
Data Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.3.4 Optimization Using Big Data Analytics . . . . . . . . . . . . . . 91
5.3.5 Decomposition of a Big-Data Job Scheduling Problem . . . 92
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Big Data Analytics for Semiconductor 1
Manufacturing
1.1 Big Data and Big Data Analytics
Big data involves datasets of sizes that exceed the capabilities of typical database
softwaretoolstocapture,store,manage,andanalyze[1].Thedefinitionofbigdata
analytics can be viewed from various perspectives. According to the definition
given by SAS Institute, big data analytics is to examine large amounts of data to
uncover hidden patterns, correlations, and other insights [2]. Over traditional data
analysis methods, big data analytics may have the following advantages [2]:
(cid:129) Big data analytics is much more efficient in processing large amounts of data.
(cid:129) Big data analytics can support real-time applications.
This definition emphasizes the efficiency of big data analytics.
Another definition is given by Russom [3], which defined big data analytics as
the application of advanced analytic techniques to big datasets. The analysis tools
commonlyusedforbigdata analyticsincludeSQL queries, data mining, statistical
analysis, fact clustering, data visualization, natural language processing, text ana-
lytics, and artificial intelligence (AI) [3–5]. In this definition, the huge amount of
data is the focus. The application of big data analytics may bring the following
benefits to enterprises: cost savings, faster and better decision-making, more cus-
tomized products and services, etc. [2]. However, the required costs and other
expenses are often unreported or ignored.
The two definitions are aggregated as in Fig. 1.1.
©TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2023 1
T.-C.T.Chen,ProductionPlanningandControlinSemiconductorManufacturing,
SpringerBriefsinAppliedSciencesandTechnology,
https://doi.org/10.1007/978-3-031-14065-5_1
2 1 BigDataAnalyticsforSemiconductorManufacturing
Fig.1.1 Definingbigdata
analyticsfromvarious Big Data
perspectives Analytics
Advanced Huge
Real-time
Analytics Amount of
Applications Techniques Data
Efficiency Effectiveness
1.2 Big Data Analytics for Manufacturing
Among the various functions of a company, manufacturing is undoubtedly the
functionthatrequirestheassistanceofbigdataanalyticsmost,becausetheamount
of data generated is the largest, the capital invested is huge, and there are
time-sensitive requirements [6]. For example, applying big data analytics to com-
pare the waiting times of tens of thousands of jobs in a wafer fabrication plant
(wafer fab) and analyze the possible reasons for their delays makes sense, because
jobs with excessively long waiting times can be identified, and appropriate actions
can be developed and taken to expedite these jobs. In addition, the defect patterns
on tens of thousands of wafers can be compared [7]. Wafers with similar defect
patterns can be clustered, and for each cluster, the causes of defects are identified
and attempted to eliminate. In practice, big data analytics is applied to analyze all
manufacturingdatathatmanagerscareabout.Someexamplesofbigdataanalytics
applications in manufacturing are given below.
Predictivemaintenanceistheuseofsensorstomonitortheconditions(suchas
temperature,pressure,noiselevel,etc.)ofamachinetoestimatethetimetothenext
failure of the machine [8]. Predictive maintenance is an application of
cyber-physical systems in Industry 4.0. If the estimated time is earlier than
expected, the scheduled predictive maintenance of the machine can be brought
forward. The reason for this is that unexpected machine failures result in larger
lossesthanexpectedones.However,continuousmonitoringusingsensorsresultsin
a large amount of data that are difficult to analyze. Wan et al. [9] transmitted the
collectedbigdatatoacloud-baseddatastorageandanalysisserviceforprocessing
(i.e., cloud computing). Anartificial neural network (ANN)wasconstructed asthe
estimation mechanism. Inputs to the ANN included the cutting speed, feed, and
cutting depth of a tool in a machining center. To reduce the amount of data, the
statistics(e.g.,means,variances,peakvalues,etc.)ratherthantherawdataofthese
production conditions were used (i.e., variable replacement). The output from the
ANN was the estimated lifetime of the tool.
1.2 BigDataAnalyticsforManufacturing 3
Cycletimereductionispursuedbyfactoriesaroundtheworld.Toachievethis,
the relationship between the features (or production conditions) and cycle times of
jobs is fitted. Based on the fitted relationship, these features (or production con-
ditions)canbeadjustedtoshortenthecycletimesofjobs.However,therearemany
possibleproductionconditionsthatcanaffectthecycletimeofajob.Inaddition,a
large,complexproductionsystemhasthousandsoreventensofthousandsofjobs,
making cycle time prediction and reduction a complex big data analysis problem.
As aresult, most past studies havefailed toaccurately estimatethecycle time ofa
job. To address this problem, in Chen and Wang [10], a group of experts was
formed,eachofwhichconstructedafuzzydeepneuralnetwork(FDNN)toestimate
the cycle time range of a job in a wafer fab instead. Then, the fuzzy weighted
intersection(FWI)[11]wasappliedtoaggregatethecycletimerangesestimatedby
all experts.
Deep learning usually means ANNs with multiple hidden layers. Some studies
haveshownthatanANNwithasinglehiddenlayermayrequirealargenumberof
neurons to handle a big data problem, which is time-consuming. In contrast,
sometimesadeepneuralnetwork(DNN)cansolveabigdataproblemquickerwith
fewer neurons [12]. How to configure a DNN for a big data problem is a critical
issue. The effectiveness and efficiency can be further enhanced by parallel pro-
cessing. For example, Raina et al. [13] constructed stacked restricted Boltzmann
machines (RBMs) to solve a big data problem with multiple processing units in
parallel.
InLietal.[14],75processparameterswereconsideredtoaffecttheformationof
eightkindsofdefectsincastings.Thevalues ofsomeprocessparameterscould be
retrievedfromthecontrolsystemofamachine,whilethoseofotherswerecollected
throughsensorsconnectedtothemachine.Thecollecteddataweretransmittedtoa
smartmiddlewarethroughanintranetforpreprocessing(includingdatastructuring,
data formalization/standardization, redundancy removal, and data framing). The
processed data were then uploaded via the Internet to a cloud-based system, on
which an ANN was constructed to predict defect types based on relevant process
parameters.
Numerousframeworksorarchitectureshavebeenestablishedtogivearoadmap
to applying big data analytics to manufacturing systems. However, although such
frameworks or architectures have attracted the attention of researchers and practi-
tioners in related fields, their practicability remains questionable. In Wang et al.
[15],softwareagentscommunicatewitheachotheronbehalfofmachinesandother
physical resources, forming an industrial network, i.e., Internet of machines
(IoM)orInternetofthings(IoT).Thebigdatacollectedfromtheindustrialnetwork
were then uploaded onto the cloud for access by statisticians and coordinators via
handheld devices.
The volume of big data leads to difficulties in data collection, storage, and
analysis.Toovercomethisdifficulty,thecollectedbigdatacanbeanalyzedtofind
anycorrelationinthedata.Forexample,futurevaluescansometimesbeestimated
from past data, or the values of some production conditions can be inferred from
those of others. In this way, data only need to be collected for part of the time or
