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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 30, NO. 4, NOVEMBER 2017
345
Exploit the Value of Production Data to Discover
Opportunities for Saving Power Consumption
of Production Tools
Chih-Min Yu, Chen-Fu Chien, Member, IEEE, and Chung-Jen Kuo
Abstract—Semiconductor industry is both technology and
energy intensive. There is a critical need to develop effective
ways for energy saving to support smart and green production. This paper aims to develop data mining approach based
on neural networks to exploit the value of production data and
derive improvement directions for energy saving. In particular,
the power consumption per wafer processed step (kilowatt hour
per move, kwh/move) of individual production tool sets can be
estimated, in which the relationships between kwh/move and
19 individual input factors, including “lot size,” “process time,”
“uptime,” “usable machine,” “Q-time constrain,” and “sampling
rate” are derived. An empirical study was conducted in a leading
wafer fab and the results have shown practical viability of the
proposed approach to discover effective opportunities for saving
17.21% power consumption by production tool sets.
Index Terms—Energy management, power saving, data mining, neural network application, manufacturing intelligence, big
data analytics, semiconductor fab.
I. I NTRODUCTION
NERGY conservation has become a critical issue in the
recent years. Furthermore, energy consumption is a critical driver for economic growth, yet it is one of key factors for
climate change [1]. Therefore, the governments have strived
to enable the policy of energy conservation and reduce carbon emission. Semiconductor industry is both technology and
energy intensive, in which effective ways are needed to reach
energy saving goal [2].
Semiconductor manufacturing is one of the most complicated manufacturing systems that contain hundreds of process
steps, in which the data is generated when wafers move from
step to step in a wafer fabrication facility (fab) [3], [4],
which involves highly complex and lengthy wafer fabrication
E
Manuscript received May 30, 2017; revised July 21, 2017 and
August 19, 2017; accepted September 5, 2017. Date of publication
September 11, 2017; date of current version October 27, 2017. This work was
supported by the Ministry of Science and Technology, Taiwan, under Grant
MOST 105-2218-E-007-028, Grant MOST 105-2218-E-007-027, and Grant
MOST-105-2622-8-007-002-TM1. (Corresponding author: Chih-Min Yu.)
C.-M. Yu is with the Department of Industrial Engineering and Engineering
Management, National Tsing Hua University, Hsinchu 30013, Taiwan,
and also with YouThought Corporation, Hsinchu 30050, Taiwan (e-mail:
richardyu@youthought.com.tw).
C.-F. Chien is with the Department of Industrial Engineering and
Engineering Management, National Tsing Hua University, Hsinchu 30013,
Taiwan (e-mail: cfchien@mx.nthu.edu.tw).
C.-J. Kuo is with YouThought Corporation, Hsinchu 30050, Taiwan (e-mail:
davidkuo@youthought.com.tw).
Digital Object Identifier 10.1109/TSM.2017.2750712
processes with 300–500 process steps and a large number
of interrelated variables [5]. In addition, the semiconductor
industry not only requires a high standard on the stability and
quality of electricity supply, but also has obviously consumed
more electricity than other industries like the steel or petrochemical. If the high-tech industries cannot put more efforts
on energy conservation, aside from paying high electricity
expenses, they could even face trade sanctions. Thus, a set
of OPE (Overall Power Energy Effectiveness) indicators were
constructed for Fab energy efficiency assessment and energy
saving [2].
Data mining and big data analytics approaches have
been developed to extract potentially useful information
and manufacturing intelligence from massive data in various problems in different fields such as bioinformatics [6],
marketing trend and demand forecast [7], sales forecast [8],
human resource management [9], financial engineering [10],
health and medical decision making [11], fault location [12],
and manufacturing [13], [14]. In particular, data mining and manufacturing intelligence approaches have been
implemented for semiconductor manufacturing including
demand forecast [7], human resource management [9], yield
enhancement [15], [16], wafer bin map analysis [17], [18],
cycle time reduction [13], [14], and fault location [19]. In
addition, various statistical analysis approaches were developed to model the relation between the responses and the
factors for the traditional manufacturing system [3]. For
a semiconductor wafer fab, the facility system accounts for
56% of the total power consumption [20], [21]. Consequently,
most of previous works focus on suppression of power consumption by the facility system. However, little research has
been done to save power consumption by process tools that
account for 41% of power consumed in the fab.
To fill the gap, this study aims to develop a data mining framework that integrates neural networks (NNs) model
to exploit the value of production big data and equipment
data to discover opportunities for saving power consumption
from production tools through manufacturing management. An
empirical study was conducted for validation.
II. M ETHODOLOGY
Data mining has been applied in various fields, in which
NNs have been employed for many data mining and machine
learning techniques [22]. Data mining problems are generally
c 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
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346
Fig. 1.
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 30, NO. 4, NOVEMBER 2017
Multi-layers feed-forward neural network with full connected.
categorized as four types of tasks: 1) regression; 2) classification; 3) clustering; and 4) association [23]. Classification
consists of examining the attributes of an object and
assigning it to one of the predefined categorical classes.
Clustering is the process of dividing a dataset into several clusters so as to minimize the intra-class similarity
and maximize the inter-class similarity. Association is the
discovery of association rules showing the conditions that
specific attribute values occur simultaneously. Data mining
has been employed as manufacturing intelligence approach
to extract useful information and derived patterns from manufacturing data to support related decisions. For example,
Hsu and Chien [17] developed a hybrid approach that integrates spatial statistics and Adaptive Resonance Theory (ART)
NNs to efficiently extract patterns from wafer bin maps associating with defects for effective trouble shooting and yield
enhancement.
NNs are data mining techniques often employed for regression analysis that have inherent capability to map nonlinear
relationship between the input and output factors [22], [24].
Since there are nonlinear relationships between input factors and the kwh/move, NNs for regression are employed for
modeling the relationships of related factors and the kwh/move
levels of individual tool sets in a semiconductor fab. To
investigate which approach will be most suitable for evaluating the relation between input factors and kwh/move of
tool sets, we compared four representative NNs for regression task including back-propagation neural networks (BPNN),
recurrent neural networks (RNN), radial basis function neural
networks (RBFNNs), and support vector regression (SVR) as
follows.
A. Back-Propagation Neural Networks (BPNN)
BPNN are multilayer feed-forward NNs with an input layer,
an output layer and some hidden layers between the input and
output layers as illustrated in Fig. 1.
B. Recurrent Neural Networks (RNN)
Comparing to BPNN that are confined to static mapping, RNN contain feedback connections and can model the
evolution of dynamic systems, as showed in Fig. 2.
Fig. 2.
Multi-layers recurrent neural network with dynamic connected.
C. Radial Basis Function Neural Networks (RBFNNs)
The major advantage of RBFNN over BPNN is the fast
convergence [25]. The hidden layer neuron in RBFNN generally uses Gaussian basis function to transfer the inputs. The
training in the hidden layer acts in an unsupervised way, unlike
the supervised way in BPNN [26].
D. Support Vector Regression (SVR)
SVR is a novel neural network algorithm based on the
concept of support vector machine classifier [27]. The SVR
learning minimizes an upper bound on the generalization error,
as opposed to typical learning algorithms that minimize the
error on the training data [28]. SVR has several advantages
over other typical NNs, such as a global and unique solution
and fast convergence [28], [29].
III. E MPERICAL S TUDY
A. Data Preparation
The data used for this study have been collected from
48 production tool sets of an 8-inch wafer foundry fab in
Singapore; and the duration for data collection is 120 days.
The tool set means a group of machine which processes
same working step in semiconductor manufacturing factory,
included each kinds of IC manufacturing process ones, like
Metal Lithography, Metal Etching, Metal Sputter, Tungsten
chemical machine polish (W CMP), and etc. Without loss
of generality, the data used for this research has been transformed to protect proprietary information of this case company. Take a tool set for example, Fig. 3 shows that the
average kwh/move decreases nonlinearly as utilization gets
higher. Hence, power consumption will be less if the variation of utilization is smaller given the same average utilization
level.
Similarly, Fig. 4 implies that the larger the lot size, the
less the kwh/move. In addition, kwh/move will be less if the
variation of lot size is smaller. The factors mentioned above
were for estimating the kwh/move levels of individual tool
sets.
By referring to Kuo et al. [30] and domain knowledge of
the case fab, this study defined 19 factors that might affect
YU et al.: EXPLOIT VALUE OF PRODUCTION DATA TO DISCOVER OPPORTUNITIES FOR SAVING POWER CONSUMPTION
347
TABLE II
C OMPARISON OF MAPE FOR D IFFERENT NN S
Fig. 3.
Kwh per move vs. tool utilization.
Table I means “Engineering lots” which lot owner belongs to
engineering department instead of production lots belonged
to manufacturing one. In addition, “COV” and “STDEV”
mean “Coefficient of Variance” and “Standard Deviation”
respectively.
B. Neural Networks (NNs) Model Construction
Fig. 4.
Kwh per move vs. mean lot size.
TABLE I
T HE I NPUT FACTORS FOR E STIMATING KWH /M OVE
the power consumption of individual tool sets in a semiconductor wafer fab, as shown in Table I. Among them,
12 factors are related to mean value while the other 7 factors are related to variation, covered by the 15 input factors
defined by Kuo et al. [30]. The terminology of “ENG” in
In this study, NNs are constructed to analyze the relationships between related input factors and kwh/move of
individual tool set in a fab. 4 learning algorithms for NNs are
evaluated. And for each algorithm, different combinations of
network architecture and parameters were experimented. We
used the package of NeuroSolutions 5 (by Neurodimensions,
Inc.) to develop the models of BPNN, RNN and RBFNN. The
package of STATISTICA 7 (by STATSOFT, Inc.) was applied
for SVR. The learning algorithms are evaluated with the
least mean absolute percent error (MAPE) for testing data, as
shown in Table II. As a result, the best NNs method is backpropagation neural networks using Levenberg-Marquardt (LM)
learning algorithm with number of hidden layer = 1 and
number of hidden neurons = 3.
Sensitivity analysis based on the trained NNs are conducted
to estimate the saved power consumption in the fab per a specific percent, e.g., 10%, of improvement on each factor, as
shown in Table III. As for the relation to kwh, the sign,
“+”, of relation between kwh/move and input factors means
positive relation, kwh/move will be higher with incremental
input factors. On the contrary, the sign, “−”, represents negative relations between kwh/move and input factors. This table
also lists the managerial implications for improving individual factors so as to take actions for saving power consumption.
However, in semiconductor manufacturing factory, there will
be some trade-off on other purpose occurred. Take “mean
percentage of super hot lots” as example, if emphasizing on
power consumption saving prior to speeding delivery, the lead
time of super hot lots must be impacted because they are not
allowed to be waited with empty load ports in next process
348
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 30, NO. 4, NOVEMBER 2017
TABLE III
R ELATION B ETWEEN KWH AND I NPUT FACTORS , AND T HEIR
M ANAGERIAL I MPLICATIONS
TABLE IV
P RIORITIZED FACTORS FOR I MPROVEMENT
TABLE V
P RIORITIZED T OOL S ETS FOR THE FACTOR OF “M EAN
P ERCENTAGE OF U SABLE M ACHINES ”
be obtained by what-if analysis based on the room for factor improvement for each factors. Table IV shows that the
case fab might save 17.21% power consumption if it aligned
all the factors to the performance of the top 25 percentile of
benchmark.
Similarly, the tool sets can also be prioritized for each factor. As shown in Table V, the top 21% tool sets (10 / 48 tool
sets) account for 70.3% (3.10%/4.41%) the impact to total
kwh of the fab for the factor “Mean percentage of usable
machines”, which means the percentage of released tools in
this semiconductor processed step.
V. C ONCLUSION
machines if other available lots could be processed at that
time. The decision making could be judged by top manager
depended on different focus points.
IV. R ESULT AND D ISCUSSION
Due to limited resources, the fab needs to prioritize the
factors so as to save more power consumption with less
effort. As shown in Table IV, the room for factor improvement is defined by the gap between the as-is factor value of
the fab and the factor value of the top 25 percentile of all
the benchmark fabs that is provided by YouThought corporation. Then the Impact percent to total kwh of the Fab can
The climate change has caused increasing natural disasters
all over the world in recent years. Semiconductor manufacturing that is energy intensive should strive for green and smart
production. This study has validated the proposed data mining framework based on NNs to estimate the kwh/move of
individual tool sets, and to analyze the relationships between
kwh/move and individual input factors. An empirical study
was conducted by using the equipment data of a real fab.
The results have validated that the proposed approach can discover useful opportunities for saving power consumption by
production tools.
Future research can be done to employ the proposed
approach for analyzing big data automatically collected in
wafer fab to derive effective directions for saving consumption
of other resources for total resource management [31], [32].
Similar approach can be extended to other high energyconsumption manufacturing such as TFT-LCD and solar cells
fabrication. On the other hand, saving power and other productivity improvement share common points in semiconductor
manufacturing factory as well. Some overall resource efficiency improvement activities will contribute the benefits
for both productivity and power saving [33]. For example, the following actions can be employed, 1) To reduce
the variation of mean available time and process time of
process machine; 2) To enlarge the mean wafer lot size
and batch size to eliminate the frequency of venting and
pumping while load and unload lots; 3) To enhance the
YU et al.: EXPLOIT VALUE OF PRODUCTION DATA TO DISCOVER OPPORTUNITIES FOR SAVING POWER CONSUMPTION
same recipe rate to avoid higher setup time while changing recipe to investigate productivity improvement directions.
More studies can be done to compare with other productivity
improvement efforts, while considering realistic manufacturing contexts such as under super hot lot, to examine the
trade-off among the factors of power saving, quality, and
productivity.
R EFERENCES
[1] A. Battaglini, J. Lilliestam, A. Haas, and A. Patt, “Development of
SuperSmart grids for a more efficient utilisation of electricity from
renewable sources,” J. Clean. Prod., vol. 17, no. 10, pp. 911–918,
2009.
[2] C.-F. Chien, J.-T. Peng, and H.-C. Yu, “Building energy saving performance indices for cleaner semiconductor manufacturing
and an empirical study,” Comput. Ind. Eng., vol. 99, pp. 448–457,
Sep. 2016.
[3] C.-F. Chien and S.-C. Chuang, “A framework for root cause detection of sub-batch processing system for semiconductor manufacturing
big data analytics,” IEEE Trans. Semicond. Manuf., vol. 27, no. 4,
pp. 475–488, Nov. 2014.
[4] C.-F. Chien, Y.-J. Chen, C.-Y. Hsu, and H.-K. Wang, “Overlay error
compensation using advanced process control with dynamically adjusted
proportional-integral R2R controller,” IEEE Trans. Autom. Sci. Eng.,
vol. 11, no. 2, pp. 473–484, Apr. 2014.
[5] C.-F. Chien, C.-Y. Hsu, and P. Chen, “Semiconductor fault detection
and classification for yield enhancement and manufacturing intelligence,” Flexible Services Manuf. J., vol. 25, no. 3, pp. 367–388,
2013.
[6] K.-S. Lin and C.-F. Chien, “Cluster analysis of genome-wide expression data for feature extraction,” Expert Syst. Appl., vol. 36, no. 2,
pp. 3327–3335, 2009.
[7] C.-F. Chien, Y.-J. Chen, and J.-T. Peng, “Manufacturing intelligence
for semiconductor demand forecast based on technology diffusion and
product life cycle,” Int. J. Product. Econ., vol. 128, no. 2, pp. 496–509,
2010.
[8] S. Thomassey, “Sales forecasts in clothing industry: The key success
factor of the supply chain management,” Int. J. Product. Econ., vol. 128,
no. 2, pp. 470–483, 2010.
[9] C.-F. Chien and L.-F. Chen, “Using rough set theory to recruit and retain
high-potential talents for semiconductor manufacturing,” IEEE Trans.
Semicond. Manuf., vol. 20, no. 4, pp. 528–541, Nov. 2007.
[10] A. Sharma and P. K. Panigrahi, “A review of financial accounting
fraud detection based on data mining techniques,” Int. J. Comput. Appl.,
vol. 39, no. 1, pp. 37–47, 2012.
[11] S. Evans, S. J. Leman, C. A. Deters, R. M. Fusaro, and H. T. Lynch,
“Automated detection of hereditary syndromes using data mining,”
Comput. Biomed. Res., vol. 30, no. 5, pp. 337–348, Oct. 1997.
[12] C.-F. Chien, S.-L. Chen, and Y.-S. Lin, “Using Bayesian network for
fault location on distribution feeder,” IEEE Trans. Power Del., vol. 17,
no. 13, pp. 785–793, Jul. 2002.
[13] A. Kusiak, “Rough set theory: A data mining tool for semiconductor
manufacturing,” IEEE Trans. Electron. Packag. Manuf., vol. 24, no. 1,
pp. 44–50, Jan. 2001.
[14] J. A. Harding, M. Shahbaz, T. Srinivas, and A. Kusiak, “Data mining in manufacturing: A review,” J. Manuf. Sci. Eng., vol. 128, no. 4,
pp. 969–976, Dec. 2006.
[15] C.-F. Chien, W.-C. Wang, and J.-C. Cheng, “Data mining for yield
enhancement in semiconductor manufacturing and an empirical study,”
Expert Syst. Appl., vol. 33, no. 1, pp. 192–198, 2007.
[16] D. Braha and A. Shmilovici, “Data mining for improving a cleaning
process in the semiconductor industry,” IEEE Trans. Semicond. Manuf.,
vol. 15, no. 1, pp. 91–101, Feb. 2002.
[17] S.-C. Hsu and C.-F. Chien, “Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor
manufacturing,” Int. J. Prod. Econ., vol. 107, no. 1, pp. 88–103,
2007.
[18] C.-W. Liu and C.-F. Chien, “An intelligent system for wafer bin
map defect diagnosis: An empirical study for semiconductor manufacturing,” Eng. Appl. Artif. Intell., vol. 26, nos. 5–6, pp. 1479–1486,
2013.
349
[19] Q. P. He and J. Wang, “Fault detection using the k-nearest neighbor rule
for semiconductor manufacturing processes,” IEEE Trans. Semicond.
Manuf., vol. 20, no. 4, pp. 345–354, Nov. 2007.
[20] Energy Use in the Semiconductor Manufacturing Industry. Washington,
DC, USA: Environ. Protect. Agency, 1998.
[21] S.-C. Hu and Y. K. Chuah, “Power consumption of semiconductor fabs
in Taiwan,” Energy, vol. 28, no. 8, pp. 895–907, Jun. 2003.
[22] K. A. Smith and J. N. D. Gupta, “Neural networks in business:
Techniques and applications for the operations researcher,” Comput.
Oper. Res., vol. 27, nos. 11–12, pp. 1023–1044, 2000.
[23] U. Fayyad, G. Piatesky-Shapiro, and P. Smyth, “The KDD process for
extracting useful knowledge from volumes of data,” Commun. ACM,
vol. 39, no. 11, pp. 27–34, 1996.
[24] M. Chambers and C. A. Mount-Campbell, “Process optimization
via neural network metamodeling,” Int. J. Prod. Econ., vol. 79, no. 2,
pp. 93–100, 2002.
[25] J. F. Briceno, H. El-Mounayri, and S. Mukhopadhyay, “Selecting an
artificial neural network for efficient modeling and accurate simulation of the milling process,” Int. J. Mach. Tools Manuf., vol. 42, no. 6,
pp. 663–674, 2002.
[26] B. Kim and K. Park, “Modeling plasma etching process using
a radial basis function network,” Microelectron. Eng., vol. 77, no. 2,
pp. 150–157, 2005.
[27] K.-J. Kim, “Financial time series forecasting using support vector machines,” Neurocomputing, vol. 55, nos. 1–2, pp. 307–319,
2003.
[28] W.-H. Chen, J.-Y. Shih, and S. Wu, “Comparison of support-vector
machines and back propagation neural networks in forecasting the six
major Asian stock markets,” Int. J. Electron. Financ., vol. 1, no. 1,
pp. 49–67, 2006.
[29] E. Byvatov, U. Fechner, J. Sadowski, and G. Schneider, “Comparison
of support vector machine and artificial neural network systems for
drug/nondrug classification,” J. Chem. Inf. Comput. Sci., vol. 43, no. 6,
pp. 1882–1889, 2003.
[30] C.-J. Kuo, C.-F. Chien, and J.-D. Chen, “Manufacturing intelligence
to exploit the value of production and tool data to reduce cycle
time,” IEEE Trans. Autom. Sci. Eng., vol. 8, no. 1, pp. 103–111,
Jan. 2011.
[31] C.-F. Chien, P. Chu, and L. Zhao, “Overall resource effectiveness (ORE)
indices for total resource management and case studies,” Int. J. Ind. Eng.,
vol. 22, no. 5, pp. 618–630, 2015.
[32] C.-F. Chien, C.-H. Hu, and Y.-F. Hu, “Overall space effectiveness (OSE)
for enhancing fab space productivity,” IEEE Trans. Semicond. Manuf.,
vol. 29, no. 3, pp. 239–247, Aug. 2016.
[33] C.-F. Chien, C.-W. Chou, and H.-C. Yu, “A novel route selection and resource allocation approach to improve the efficiency of
manual material handling system in 200-mm wafer fabs for industry 3.5,” IEEE Trans. Autom. Sci. Eng., vol. 13, no. 4, pp. 1567–1580,
Oct. 2016.
Chih-Min Yu received the B.S. degree in economics
and industrial engineering and the M.S. degree
in engineering management from National Tsing
Hua University, Hsinchu, Taiwan, in 1996 and 2006,
respectively, where he is currently pursuing the
Ph.D. degree with the Industrial Engineering and
Engineering Management. He joined YouThought
Company, big data solution provider for manufacturing, Taiwan, in 2013, where he is the Sales VP. He
was a Department Manager with VisEra Technology
Company, an IE Principal Engineer with Taiwan
Semiconductor Manufacturing Company, and a Manufacturing Supervisor
with Macronix International Company for over 14 years.
350
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 30, NO. 4, NOVEMBER 2017
Chen-Fu Chien received the B.S. (Phi Tao Phi
Hons.) degree with double majors in industrial engineering and electrical engineering from National
Tsing Hua University (NTHU), Hsinchu, Taiwan, in
1990, the M.S. degree in industrial engineering, and
the Ph.D. degree in operations research and decision
sciences from the University of Wisconsin–Madison,
Madison, WI, USA, in 1994 and 1996, respectively, and the PCMPCL Training from Harvard
Business School, Boston, MA, USA, in 2007. He
is a Tsing Hua Chair Professor with NTHU. He
is also the Director of the NTHU-Taiwan Semiconductor Manufacturing
Company (TSMC) Center for Manufacturing Excellence and the Principal
Investigator for the NSC Semiconductor Technologies Empowerment Partners
Consortium. From 2002 to 2003, he was a Fulbright Scholar with the
University of California at Berkeley, Berkeley, CA, USA. From 2005 to
2008, he had been on-leave as a Deputy Director with Industrial Engineering
Division, TSMC. He has received eight invention patents on semiconductor
manufacturing and published four books, over 120 journal papers, and a number of case studies in Harvard Business School. His research efforts center on
decision analysis, modeling, and analysis for semiconductor manufacturing,
manufacturing strategy, and manufacturing intelligence. He was a recipient
of the National Quality Award, the Executive Yuan Award for Outstanding
Science and Technology Contribution, the Distinguished Research Awards,
the Tier 1 Principal Investigator (Top 3%) from the Ministry of Science
& Technology, the Distinguished University-Industry Collaborative Research
Award from the Ministry of Education, the University Industrial Contribution
Awards from the Ministry of Economic Affairs, the Distinguished UniversityIndustry Collaborative Research Award, the Distinguished Young Faculty
Research Award from NTHU, the Distinguished Young Industrial Engineer
Award, the Best IE Paper Award, the IE Award from the Chinese Institute of
Industrial Engineering, the Best Engineering Paper Award, the Distinguished
Engineering Professor by Chinese Institute of Engineers in Taiwan, and the
2012 Best Paper Award of the IEEE T RANSACTIONS ON AUTOMATION
S CIENCE AND E NGINEERING. He has been invited to give keynote lectures
at several conferences, including APIEMS, C&IE, IEEM, IML, and leading universities worldwide. He is an Area Editor of the Flexible Services
and Manufacturing Journal, an Editorial Board Member of Computers and
Industrial Engineering, and an Advisory Board Member of OR Spectrum.
Chung-Jen Kuo received the B.S., M.S., and Ph.D.
degrees in industrial engineering from National
Tsing Hua University, Hsinchu, Taiwan, in 1989,
2002, and 2010, respectively. He was a Department
Manager with Macronix International Company
from 1999 to 2003, and a Project Manager with
Taiwan Semiconductor Manufacturing Company
from 2003 to 2006. In 2007, he found Youthought
Company, Taiwan, where he is currently the
Chairman and the President. His research interests
include data mining, performance evaluation, and
production management for semiconductor manufacturing.
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