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Artificial Neural Networks in Production Scheduling and Yield Prediction of Semiconductor Wafer Fabrication System [chapter]

Jie Zhang, Junliang Wang, Wei Qin
2016 Artificial Neural Networks - Models and Applications  
The production scheduling and yield prediction are two critical issues in the operation of semiconductor wafer fabrication system (SWFS).  ...  With the development of artificial intelligence, the artificial neural networks (ANN) are widely used in the control, decision-making and prediction of complex discrete event manufacturing systems.  ...  FNN is also an effective methodology for prediction of discrete event manufacturing systems, control and decisionmaking [14, 15] .  ... 
doi:10.5772/63444 fatcat:nwamcuafkzgp3fysvfagug5wjy

A Hybrid Approach Combining Fuzzy c-Means-Based Genetic Algorithm and Machine Learning for Predicting Job Cycle Times for Semiconductor Manufacturing

Gyu M. Lee, Xuehong Gao
2021 Applied Sciences  
To effectively predict job cycle time in semiconductor fabrication factories, we propose an effective hybrid approach combining the fuzzy c-means (FCM)-based genetic algorithm (GA) and a backpropagation  ...  Job cycle time is the cycle time of a job or the time required to complete a job. Prediction of job cycle time is a critical task for a semiconductor fabrication factory.  ...  Chen [2] developed a fuzzy BPN to incorporate production-control expert judgments with expert opinions to enhance the performance of an existing crisp BPN for predicting the output times of wafer lots  ... 
doi:10.3390/app11167428 fatcat:le4dfyxwobb5viecq52glvym7i

Wafer Edge Yield Prediction Using a Combined Long Short-Term Memory and Feed-Forward Neural Network Model for Semiconductor Manufacturing

Dasol Kim, Mintae Kim, Wooju Kim
2020 IEEE Access  
However, these lot-based prediction models have limitations because only sampling and measuring two to three wafers in one lot that includes 24 wafers may entail an excessive assumption.  ...  Their strategy involved a neural network and memory-based learning for lot-based yield prediction in semiconductor manufacturing [1] .  ...  DASOL KIM is currently pursuing an M.S. in industrial engineering at Yonsei University. He has been an engineer in the Memory Yield Enhancement Team of Samsung Electronics since 2013.  ... 
doi:10.1109/access.2020.3040426 fatcat:uchzibf7rzahfpkzymvod6ng44

Modelling and Control of Production Systems based on Observed Inter-event Times: An Analytical and Empirical Investigation (Ph.D. Thesis) [article]

Nima Manafzadeh Dizbin
2022 arXiv   pre-print
In the second part of the thesis, an exploratory data analysis is conducted by using a large industrial dataset related to the flow of the products at a semiconductor wafer fabrication.  ...  Then, the cycle times of the products are predicted using Machine Learning methods, and the performance of different prediction algorithms is assessed.  ...  Sha and Hsu design a three-layer Multi-Layer Perceptron for cycle time prediction of wafer lots.  ... 
arXiv:2204.01079v1 fatcat:myhpx6yzjbefxp3epn5ad6bvua

Combining SOM and GA-CBR for Flow Time Prediction in Semiconductor Manufacturing Factory [chapter]

Pei-Chann Chang, Yen-Wen Wang, Chen-Hao Liu
2006 Lecture Notes in Computer Science  
In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Case-Based Reasoning (CBR) for flow time prediction in semiconductor manufacturing factory is proposed.  ...  The flow time and related shop floor status are collected and fed into the SOM for classification. Then, corresponding GA-CBR is selected and applied for flow time prediction.  ...  This research discussed how to integrate the SOM and GA-CBR approaches to construct a hybrid system of flow time prediction.  ... 
doi:10.1007/11908029_79 fatcat:dmvadr4jnrgqhcb2ze6qenhscm

Using simulation and hybrid sequencing optimization for makespan reduction at a wet tool

Anna Rotondo, John Geraghty, Paul Young
2012 Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)  
The impact of sequencing optimization on makespan performance at a wet tool is investigated here by means of a hybrid optimization model that combines an exact optimization approach, based on an efficient  ...  When rigid scheduling rules apply to wet tools, the development of Cycle Time (CT) optimization strategies becomes a relevant challenge.  ...  A recipe describes the sequence of tanks that a lot should visit; an operation defines the processing times in each tank. Wafers are processed in lots of the same type.  ... 
doi:10.1109/wsc.2012.6465086 dblp:conf/wsc/RotondoGY12 fatcat:ssid2dkqnzhghk6y3nv42gtnna

Data mining in manufacturing: a review based on the kind of knowledge

A. K. Choudhary, J. A. Harding, M. K. Tiwari
2008 Journal of Intelligent Manufacturing  
Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases.  ...  The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis.  ...  Yarn et al. [97] presented an intelligent predictive decision support system(IPDSS) for condition based maintenance.  ... 
doi:10.1007/s10845-008-0145-x fatcat:rt4aytttffhe3fl7memlodiysa

Stochastic wafer fabrication scheduling

Youxun Shen, R.C. Leachman
2003 IEEE transactions on semiconductor manufacturing  
Moreover, the SLQ scheduling model removes a major weakness in applying dynamic programming to production control by accommodating noninteger values for lead times.  ...  To meet the challenge of integrating uncertainty analysis into wafer fabrication scheduling, this paper proposes a stochastic dynamic programming model for scheduling new releases and bottleneck processing  ...  Yano for their constructive comments. Also, the paper has been greatly im-proved as a result of the attention given to it by the Associate Editor, Dr. S. Hood, and two anonymous reviewers.  ... 
doi:10.1109/tsm.2002.807743 fatcat:c53wxjwshjb3fhyrvyrgtpzapi

Special Issue on Modeling, Simulation, Operation and Control of Discrete Event Systems

Zhiwu Li, Mengchu Zhou, Naiqi Wu, Yi-sheng Huang
2018 Applied Sciences  
An output feedback controller is proposed that can stabilize non-autonomous hybrid systems by formulating the considered problem as a linear program.  ...  The first paper [7], authored by Yao and Li, investigates the input-output finite time stabilization of time-varying impulsive positive hybrid systems based on finite state machines through the mode-dependent  ...  The 10th paper [16] , authored by Wang, Hsu, and Tran, deals with an automated material handling problem in the semiconductor industry for 450 mm wafer fabrication, where traffic-jam problems and lot-prioritization  ... 
doi:10.3390/app8020202 fatcat:57esb6xgbrcahdupjhonwwflly

A Gaussian Mixture Model Clustering Ensemble Regressor for Semiconductor Manufacturing Final Test Yield Prediction

Dan Jiang, WeiHua Lin, Nagarajan Raghavan
2021 IEEE Access  
It is therefore important for semiconductor manufacturers to detect wafer material related low yield problems at an earlier stage for effective cost and quality control.  ...  This is a challenging goal as the input data used for prediction is at a very early manufacturing stage and the output FT yield for packaged chips is the last stage of the fabrication chain.  ...  [13] for manufacturing cycle time (CT) prediction and in Ref. [14] for etching process fault detection.  ... 
doi:10.1109/access.2021.3055433 fatcat:cewngpoc4zalnmyjasmhmv7xum

Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer

Lukas Lingitz, Viola Gallina, Fazel Ansari, Dávid Gyulai, András Pfeiffer, Wilfried Sihn, László Monostori
2018 Procedia CIRP  
This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system.  ...  Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners  ...  This research work has been performed in the EU project Power Semiconductor and Electronics Manufacturing 4.0 (SemI40), which is funded by the programme Electronic Component Systems for European Leadership  ... 
doi:10.1016/j.procir.2018.03.148 fatcat:zz5wqzngnzfphmd42xgopyx66y

2019 6th International Conference on Systems and Informatics

2019 2019 6th International Conference on Systems and Informatics (ICSAI)  
These submissions cover various related areas, such as Control and Automation Systems, Power and Energy Systems, Intelligent Systems, Computer Systems and Applications, Communications and Networking, Image  ...  The demands for systems and informatics have been constantly increasing , as more and more computer applications have been built.  ...  Original ZG Control Failure for TVNS Systems Output Tracking due to States Tending to Infinity Design of Virtual Interaction Experience Based on Multi-signal Extraction of Lower Lumbar Muscle Pedestrian  ... 
doi:10.1109/icsai48974.2019.9010230 fatcat:eovojg6yirhfhnmxzugrvzzc5a

PREDICTION OF HIGH CYCLE TIMES IN WHEEL RIM MOLDING WITH ARTIFICIAL NEURAL NETWORKS

İnanç KABASAKAL, Fatma DEMİRCAN KESKİN
2021 Verimlilik Dergisi  
Methodology: Our study firstly determines thresholds for high cycle times with two alternate approaches. Subsequently, data were labeled regarding the cycle-time threshold.  ...  Alternate models based on Artificial Neural Networks (ANNs) were developed in R to predict high cycle times.  ...  Chen (2007) handled the output time prediction problem by firstly classifying the wafer lots with the k-means algorithm.  ... 
doi:10.51551/verimlilik.988472 fatcat:fiuvsfwpn5hg5m2hdojlp53wbu

Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems

Danilo Avola, Luigi Cinque, Giuseppe Placidi
2012 Intelligent Information Management  
The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems.  ...  In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions.  ...  In this way, for example, a prediction about a lot of targets can be done (e.g., life of a component, the state of an entity, the safety state of a tool).  ... 
doi:10.4236/iim.2012.46038 fatcat:rofswz5xanemzncrd65ntfxuvq

Progress and perspectives in dry processes for leading-edge manufacturing of devices: toward intelligent processes and virtual product development

Taku Iwase, Yoshito Kamaji, Song Yun Kang, Kazunori Koga, Nobuyuki Kuboi, Moritaka Nakamura, Nobuyuki Negishi, Tomohiro Nozaki, Shota Nunomura, Daisuke Ogawa, Mitsuhiro Omura, Tetsuji Shimizu (+8 others)
2019 Japanese Journal of Applied Physics  
To control such processing, methods for process monitoring, equipment control, modeling and simulation, and controlling plasma-induced damage, are required.  ...  Here, we conduct a systematic review of the literature over the last 40 years to evaluate the history and progress of dry processes in regard to intelligent process-control.  ...  Reference 181 used the signals accumulated from an etching system for real-time data collection and statistical analysis of etching properties (Fig. 10 ).  ... 
doi:10.7567/1347-4065/ab163b fatcat:ug7te4csnrdtjcqjekmmbjyi64
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