Wafer Fabrication Factory Research Articles

A fuzzy-neural approach for optimizing the performance of job dispatching in a wafer fabrication factory

A fuzzy-neural approach is presented in this study to optimize the performance of job dispatching in a wafer fabrication factory. The traditional optimization methods in this field have a few problems. To tackle these problems, we performed several treatments. First, we applied a more effective fuzzy-neural approach to estimate the remaining cycle time of a job. Then we established a systematic procedure to determine the optimal values of the parameters in the two-factor tailored nonlinear fluctuation smoothing rule for the mean cycle time, in order to optimize the scheduling performance. To assess the effectiveness of the proposed methodology, we conducted a production simulation. According to the experimental results, the proposed methodology is better than the existing approaches in optimizing the average cycle time.

A fuzzy rule for job dispatching in a wafer fabrication factory—a simulation study

Job scheduling in wafer fabrication factories is subject to many sources of uncertainty or randomness. To consider the uncertainty and improve the scheduling performance in a wafer fabrication factory, this paper proposes an innovative fuzzy rule that solves the problem of slack overlapping in a non-subjective way. The fuzzy rule considers the uncertainty in the remaining cycle time and is aimed at the simultaneous optimization of the average cycle time and cycle time standard deviation. Few existing publications discuss this issue. A systematic procedure has also been established to optimize the value of the adjustable parameter in the fuzzy rule. On the other hand, a novel fuzzy back propagation network approach is also proposed in this paper to estimate the remaining cycle time accurately. The performance of the proposed methodology is evaluated with a series of production simulation experiments.

An Iterative Procedure for Optimizing the Performance of the Fuzzy-Neural Job Cycle Time Estimation Approach in a Wafer Fabrication Factory

Estimating the cycle time of each job in a wafer fabrication factory is a critical task to every wafer manufacturer. In recent years, a number of hybrid approaches based on job classification (either preclassification or postclassification) for cycle time estimation have been proposed. However, the problem with these methods is that the input variables are not independent. In order to solve this problem, principal component analysis (PCA) is considered useful. In this study, a classifying fuzzy-neural approach, based on the combination of PCA, fuzzy c-means (FCM), and back propagation network (BPN), is proposed to estimate the cycle time of a job in a wafer fabrication factory. Since job classification is an important part of the proposed methodology, a new index is proposed to assess the validity of the classification of jobs. The empirical relationship between theSvalue and the estimation performance is also found. Finally, an iterative process is employed to deal with the outliers and to optimize the overall estimation performance. A real case is used to evaluate the effectiveness of the proposed methodology. Based on the experimental results, the estimation accuracy of the proposed methodology was significantly better than those of the existing approaches.

A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory

This paper proposes a fuzzy slack-diversifying fluctuation-smoothing rule to enhance the scheduling performance in a wafer fabrication factory. The proposed rule considers the uncertainty in the remaining cycle time and is aimed at simultaneous improvement of the average cycle time, cycle time standard deviation, the maximum lateness, and number of tardy jobs. Existing publications rarely discusse ways to optimize all of these at the same time. An important input to the proposed rule is the job remaining cycle time. To this end, this paper proposes a self-adjusted fuzzy back propagation network (SA-FBPN) approach to estimate the remaining cycle time of a job. In addition, a systematic procedure is also established, which can solve the problem of slack overlapping in a nonsubjective way and optimize the overall scheduling performance. The simulation study provides evidence that the proposed rule can improve the four performance measures simultaneously.

Precise and Accurate Job Cycle Time Forecasting in a Wafer Fabrication Factory with a Fuzzy Data Mining Approach

Many data mining methods have been proposed to improve the precision and accuracy of job cycle time forecasts for wafer fabrication factories. This study presents a fuzzy data mining approach based on an innovative fuzzy backpropagation network (FBPN) that determines the lower and upper bounds of the job cycle time. Forecasting accuracy is also significantly improved by a combination of principal component analysis (PCA), fuzzy c-means (FCM), and FBPN. An applied case that uses data collected from a wafer fabrication factory illustrates this fuzzy data mining approach. For this applied case, the proposed methodology performs better than six existing data mining approaches.

A Fuzzy Nonlinear Programming Approach for Optimizing the Performance of a Four-Objective Fluctuation Smoothing Rule in a Wafer Fabrication Factory

In theory, a scheduling problem can be formulated as a mathematical programming problem. In practice, dispatching rules are considered to be a more practical method of scheduling. However, the combination of mathematical programming and fuzzy dispatching rule has rarely been discussed in the literature. In this study, a fuzzy nonlinear programming (FNLP) approach is proposed for optimizing the scheduling performance of a four-factor fluctuation smoothing rule in a wafer fabrication factory. The proposed methodology considers the uncertainty in the remaining cycle time of a job and optimizes a fuzzy four-factor fluctuation-smoothing rule to sequence the jobs in front of each machine. The fuzzy four-factor fluctuation-smoothing rule has five adjustable parameters, the optimization of which results in an FNLP problem. The FNLP problem can be converted into an equivalent nonlinear programming (NLP) problem to be solved. The performance of the proposed methodology has been evaluated with a series of production simulation experiments; these experiments provide sufficient evidence to support the advantages of the proposed method over some existing scheduling methods.

Internal Due Date Assignment in a Wafer Fabrication Factory by an Effective Fuzzy-Neural Approach

Owing to the complexity of the wafer fabrication, the due date assignment of each job presents a challenging problem to the production planning and scheduling people. To tackle this problem, an effective fuzzy-neural approach is proposed in this study to improve the performance of internal due date assignment in a wafer fabrication factory. Some innovative treatments are taken in the proposed methodology. First, principal component analysis (PCA) is applied to construct a series of linear combinations of the original variables to form a new variable, so that these new variables are unrelated to each other as much as possible, and the relationship among them can be reflected in a better way. In addition, the simultaneous application of PCA, fuzzy c-means (FCM), and back propagation network (BPN) further improved the estimation accuracy. Subsequently, the iterative upper bound reduction (IUBR) approach is proposed to determine the allowance that will be added to the estimated job cycle time. An applied case that uses data collected from a wafer fabrication factory illustrates this effective fuzzy-neural approach.

A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory

This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the rule’s effectiveness. The findings in this research point to several directions that can be exploited in the future.

A post-classifying fuzzy-neural and data-fusion rule for job scheduling in a wafer fab - a simulation study

This paper proposed a post-classifying fuzzy-neural and data-fusion rule to improve the performance of job scheduling in a wafer fabrication factory (wafer fab). The proposed rule is a hybrid (fusion) of two well-known fluctuation smoothing rules – FSMCT and FSVCT. Several ways of data fusion [including normalised sum (NS), normalised product (NP), condensed normalised product (CNP), weighted normalised product (WNP), and dynamic weighted normalised product (DWNP)] were applied for this purpose. Besides, in order to enhance the scheduling performance of the rule, the post-classifying fuzzy back propagation network (FBPN) approach was applied to improve the forecasting accuracy of the remaining cycle time. To evaluate the effectiveness of the proposed methodology, a production simulation was carried out. According to the experimental results, the proposed methodology outperformed some existing approaches by simultaneously reducing the average cycle time and cycle time variation.

A Fuzzy-Neural Ensemble and Geometric Rule Fusion Approach for Scheduling a Wafer Fabrication Factory

In this study, the fuzzy-neural ensemble and geometric rule fusion approach is presented to optimize the performance of job dispatching in a wafer fabrication factory with an intelligent rule. The proposed methodology is a modification of a previous study by fusing two dispatching rules and diversifying the job slacks in novel ways. To this end, the geometric mean of the neighboring distances of slacks is maximized. In addition, the fuzzy c-means (FCM) and backpropagation network (BPN) ensemble approach was also proposed to estimate the remaining cycle time of a job, which is an important input to the new rule. A new aggregation mechanism was also designed to enhance the robustness of the FCM-BPN ensemble approach. To validate the effectiveness of the proposed methodology, some experiments have been conducted. The experimental results did support the effectiveness of the proposed methodology.

An effective dispatching rule for bi-objective job scheduling in a wafer fabrication factory—considering the average cycle time and the maximum lateness

This paper presents an effective dispatching rule to improve the scheduling of jobs in a wafer fabrication factory. Modified from two traditional dispatching rules, the new dispatching rule is aimed at the simultaneous optimization of the average cycle time and the maximum lateness; in practice, this means a promise of delivery to the customer as soon as possible and strict commitment to that promise. This simultaneous optimization has rarely been discussed in the past. The proposed methodology has the following innovative features. First, a highly effective fuzzy–neural approach is applied to estimate the remaining cycle time of a job. Second, the fluctuation smoothing rule for mean cycle time (FSMCT) and the earliest due date (EDD) rule are fused in a nonlinear way to generate the new rule. We also show that there is a contradiction between FSMCT and EDD and establish a certain-rule-first procedure to resolve the contradiction. To assess the effectiveness of the proposed methodology, production simulation is also applied in this study. The experimental results show the proposed methodology is better than some existing approaches at simultaneously reducing the average cycle time and the maximum lateness.

A Fuzzy Fluctuation Smoothing Rule for Job Dispatching in a Wafer Fabrication Factory

Job dispatching in a wafer fabrication factory is a difficult task, mainly due to the complexity of the production system and the uncertainty involved in the production activities. Recently, a number of advanced dispatching rules were proposed, which estimate the remaining cycle times of jobs. This predictive nature is conducive to the effectiveness of these rules. If the uncertainty in the remaining cycle time can be better considered, incorrect scheduling will possibly be reduced. The tailored nonlinear fluctuation smoothing rule for mean cycle time (TNFSMCT) is fuzzified in this study, by expressing the remaining cycle time with a fuzzy value. The effectiveness of the proposed methodology is illustrated with a simulation study.

A slack-diversifying nonlinear fluctuation smoothing rule for job dispatching in a wafer fabrication factory

This study proposes a slack-diversifying nonlinear fluctuation smoothing rule to reduce the average cycle time in a wafer fabrication factory. The slack-diversifying nonlinear fluctuation smoothing rule is derived from the one-factor tailored nonlinear fluctuation smoothing rule for cycle time variation (1f-TNFSVCT) by dynamically maximizing the standard deviation of the slack, which has been shown to improve scheduling performance in several previous studies. The effectiveness of the proposed rule has been validated via using it with a simulated data set. Based on the findings in this research we also derived several directions that can be exploited in the future.

Removing of Nano-Particles from Semiconductor Wastewater Using a Hybrid Treatment System

Packaging process is one of the main manufacturing steps in the wafer fabrication industries. However, nano-particles would be produced during the packaging process. The produced nano-particle-contained wastewater has characteristics of dark color and high turbidity. Because the nano-particles would usually result in the clogging of the membrane filtration system when it is used for water treatment and reclamation, the application of a pre-treatment system is required to extend the membrane life. The objective of this study was to develop a pre-treatment system for packaging wastewater treatment before membrane system was applied for further water quality improvement. In this laboratory-scale study, a hybrid treatment system containing a chemical coagulation/flocculation followed by ultra-filtration (UF) membrane technology was developed for the wafer fabrication wastewater treatment. The chemical coagulation/flocculation unit was used as the pre-treatment process to improve the efficiency of the following ultra-filtration (UF) membrane system. The packaging wastewater was collected from a wafer fabrication factory and used to evaluate the feasibility of the coagulation/flocculation process on nano-scale particle removal. Results show that approximately 98% of turbidity could be removed at pH 7 when 2.2 mg/L of polyaluminum chloride (PAC) (used as coagulant) and 0.5 mg/L of polyacrylamide (cPAM) (used as flocculant) were added during the coagulation/flocculation process. Results indicate that the coagulation/flocculation is a feasible pre-treatment process for nano-particle removal before UF membrane is applied for further water purification. Results from this study will be helpful in designing a scale-up system for practical applications.

The application of dynamic programming to control wafers inventory problem

In the field of production and operations management, inventory problem has always been considered as an important topic. This paper studies the control wafers inventory problem in wafer fabrication factories. A single-period, multi-product inventory problem with reentry and downward substitution was examined in a pulling control production environment. The control wafers inventory problem is firstly constructed as a network, and latter dynamic programming is applied next to solve the problem. The objective is to set an acceptable inventory level to minimize the total cost of control wafers through reducing various types of costs without halting production throughput. A numerical example is given to illustrate the practicality of the model. The results demonstrate that the proposed model is an effective tool for determining the inventory level of control wafers for each grade.

A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory

A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, a more effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future.

A localised fuzzy-neural fluctuation smoothing rule for job scheduling in a wafer fab

This paper presents a localised fuzzy-neural fluctuation smoothing rule to improve the performance of scheduling jobs in a wafer fabrication factory (wafer fab). The rule is modified from the tailored non-linear fluctuation smoothing (TNFS) rule with some innovative treatments. First, the remaining cycle time of a job is forecasted with an evolving fuzzy-neural approach in order to improve the accuracy. Second, in the original TNFS rule, the adjustable factor is static, and in this rule it becomes dynamic. Third, the adjustable factor in the new rule depends on the jobs gathering before the same machine, and the TNFS rule becomes localised. To assess the effectiveness of the proposed methodology, production simulation is also applied in this study. According to the experimental results, the proposed methodology is better than some existing approaches in reducing the average cycle time and cycle time standard deviation.

Enhancing Scheduling Performance for a Wafer Fabrication Factory: The Biobjective Slack-Diversifying Nonlinear Fluctuation-Smoothing Rule

A biobjective slack-diversifying nonlinear fluctuation-smoothing rule (biSDNFS) is proposed in the present work to improve the scheduling performance of a wafer fabrication factory. This rule was derived from a one-factor bi-objective nonlinear fluctuation-smoothing rule (1f-biNFS) by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several previous studies. The efficacy of the biSDNFS was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future.

A job-classifying and data-mining approach for estimating job cycle time in a wafer fabrication factory

Before making a due date commitment to our customer, we need to estimate the cycle times of jobs. For this reason, many of the most-advanced methods classify jobs, before or after estimating the cycle times. However, job classification is not directly helpful to optimize the performances of these estimation methods. To solve this problem, a job-classifying and data-mining approach is proposed in this study to improve the performance of cycle time estimation by optimizing the results of job classification. In addition, some association rules are also extracted from the estimation results to facilitate the practical application of the proposed methodology. According to the results of a case study, the job-classifying and data-mining approach achieved a better estimation performance and could produce some useful estimation rules.

A Dynamically Optimized Fluctuation Smoothing Rule for Scheduling Jobs in a Wafer Fabrication Factory

This paper presents a dynamically optimized fluctuation smoothing rule to improve the performance of scheduling jobs in a wafer fabrication factory. The rule has been modified from the four-factor bi-criteria nonlinear fluctuation smoothing (4f-biNFS) rule, by dynamically adjusting factors. Some properties of the dynamically optimized fluctuation smoothing rule were also discussed theoretically. In addition, production simulation was also applied to generate some test data for evaluating the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology was better than some existing approaches to reduce the average cycle time and cycle time standard deviation. The results also showed that it was possible to improve the performance of one without sacrificing the other performance metrics.