A fuzzy-neural system with error feedback to adjust classification for forecasting wafer lot flow time: A simulation study

2007 ◽  
Vol 221 (5) ◽  
pp. 807-817 ◽  
Author(s):  
T Chen ◽  
Y-C Wang
Keyword(s):  
Back Propagation ◽  
Neural System ◽  
Estimation Accuracy ◽  
Forecasting Accuracy ◽  
Forecasting Error ◽  
Fuzzy Neural ◽  
Suitable Combination ◽  
Flow Cycle ◽  
Generate Test Data ◽  
Propagation Network

Estimating lot flow (cycle) time is a critical task for a wafer fabrication plant (wafer fab). Many recent studies have shown that pre-classifying wafer lots before estimating the flow times is beneficial to estimation accuracy. In this aspect, various classification approaches, e.g. k-means (kM), fuzzy c-means (FCM), and self-organization map (SOM), have been applied. After pre-classification, to estimate the flow times for lots belonging to different categories, different approaches (that are in fact the same approaches but with different parameter settings) are applied. However, these applications of classification approaches considered only the data of wafer lots, but ignored whether the classification approaches combined with the subsequent estimation techniques were suitable for the data. To tackle this problem, instead of trying many possible classification and forecasting approaches to find out the most suitable combination, a FCM and back propagation network (BPN) combination is chosen in the current study. In the proposed methodology, the classification results by FCM will be adjusted with forecasting error fed back from the BPN. In this way, if the FCM-BPN combination is not good enough for the data, then a forecasting error will be generated and fed back to the FCM classifier to adjust the classification results. After some replications, the FCM-BPN combination will become more suitable for the data. To evaluate the effectiveness, production simulation is applied in the present study to generate test data. According to experimental results, the forecasting accuracy of the proposed methodology is significantly better than those of many existing approaches. The effects of adjusting classification results with prediction error are also revealed.

A FUZZY-NEURAL SYSTEM INCORPORATING UNEQUALLY IMPORTANT EXPERT OPINIONS FOR SEMICONDUCTOR YIELD FORECASTING

2008 ◽  
Vol 16 (01) ◽  
pp. 35-58 ◽  
Author(s):  
TOLY CHEN ◽  
YU-CHENG LIN
Keyword(s):  
Semiconductor Manufacturing ◽  
Back Propagation ◽  
Neural System ◽  
Expert Opinions ◽  
Fuzzy Neural ◽  
Yield Forecast ◽  
Precision And Accuracy ◽  
Yield Forecasting ◽  
Propagation Network ◽  
Multiple Experts

Yield forecasting is a very important task to a semiconductor manufacturing factory. To enhance both the precision and accuracy of semiconductor yield forecasting, a fuzzy-neural system incorporating unequally important expert opinions is constructed in this study. In the proposed methodology, multiple experts construct their own fuzzy yield learning models from various viewpoints to predict the yield of a product. Besides, these expert opinions can also be considered unequally important. To aggregate these fuzzy yield forecasts, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy yield forecasts into a polygon-shaped fuzzy yield forecast, in order to improve the precision of yield forecasting. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy yield forecast and to generate a representative/crisp value, so as to enhance the accuracy. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, all approaches were applied to the practical data of three products in a real semiconductor manufacturing factory. According to experimental results, the proposed methodology improved both the precision and accuracy of semiconductor yield forecasting by 48% and 38%, respectively.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Toly Chen
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Due Date ◽  
Planning And Scheduling ◽  
Simultaneous Application ◽  
Due Date Assignment ◽  
Fuzzy Neural ◽  
Propagation Network

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.

scholarly journals Development of a Practical Method to Estimate the Eco-Level of Driver Performance

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jianbin Zheng ◽  
Yiping Wu
Keyword(s):  
Fuel Consumption ◽  
Fuel Efficiency ◽  
Back Propagation ◽  
Practical Method ◽  
Estimation Accuracy ◽  
Road Transportation ◽  
Estimation Model ◽  
Driver Performance ◽  
Study Results ◽  
Propagation Network

Motor vehicle’s fuel consumption is one of the main sources of energy consumption in road transportation and is highly influenced by driver performance in the process of driving. Eco-driving behavior has been proved to be an effective way to improve the fuel efficiency of vehicles. Essential to the efforts towards saving vehicle fuel is the need to estimate the eco-level of driver performance accurately and practically. Depending on on-board diagnostics and Global Position devices, individual vehicle’s instantaneous fuel consumption, engine revolution and torque, speed, acceleration, and dynamic location were collected. Back-propagation network was adopted to explore the relationship between vehicle fuel consumption and the parameters of driver performance. Taking 700 data samples in basic segments of urban expressways as our training set and 100 data samples as validation test, we found the optimal model structure and parameters through repeated simulation experiments. In addition to the average and standard deviation value, the fluctuation frequency of driver performance data was also viewed as influence factors in eco-level estimation model. The average estimation accuracy of our developed model has been tested to be 96.37%, which is quite higher than that of linear regression model. The study results provide a practical way to evaluate drivers’ performance from the perspective of fuel consumption and thus give basis for rewarding best drivers within eco-driving programs.

Performance Optimization of Extrusion Blow Molded Parts Using Fuzzy Neural-Taguchi Method and Genetic Algorithm

2002 ◽  
Author(s):  
Jyh-Cheng Yu ◽  
Tsung-Ren Hung ◽  
Francis Thibault
Keyword(s):  
Genetic Algorithm ◽  
Performance Optimization ◽  
Fitness Function ◽  
Back Propagation ◽  
Stress Constraints ◽  
Molding Process ◽  
Finite Element Software ◽  
Blow Molding ◽  
Fuzzy Neural ◽  
Propagation Network

This paper presents a soft computing strategy to determine the optimal die gap parison programming of extrusion blow molding process. The design objective is to minimize part weight subject to stress constraints. The finite-element software, BlowSim, is used to simulate the parison extrusion and the blow molding processes. However, the simulations are time consuming, and minimizing the number of simulation becomes an important issue. The proposed strategy, Fuzzy Neural-Taguchi and Genetic Algorithm (FUNTGA), first establishes a back propagation network using Taguchi’s experimental array to predict the relationship between design variables and response. Genetic algorithm is then applied to search for the optimum design of parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The Reliability Distance is proposed and introduced to genetic algorithm using fuzzy rules to modify the fitness function and thus improve search efficiency. This study uses ANSYS to find the stress distribution of blown parts under loads. The comparison of results demonstrates the effectiveness of the proposed strategy.

Job remaining cycle time estimation with a post-classifying fuzzy-neural approach in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1021-1031 ◽  
Author(s):  
T Chen
Keyword(s):  
Test Data ◽  
Simulation Study ◽  
Cycle Time ◽  
Estimation Error ◽  
Back Propagation ◽  
Time Estimation ◽  
Wafer Fabrication ◽  
Fuzzy Neural ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

A post-classifying fuzzy-neural approach is proposed in this study for estimating the remaining cycle time of each job in a wafer fabrication plant, which has seldom been investigated in past studies but is a critical task for the wafer fabrication plant. In the methodology proposed, the fuzzy back-propagation network (FBPN) approach for job cycle time estimation is modified with the proportional adjustment approach to estimate the remaining cycle time instead. Besides, unlike existing cycle time estimation approaches, in the methodology proposed a job is not preclassified but rather post-classified after the estimation error has been generated. For this purpose, a back-propagation network is used as the post-classification algorithm. To evaluate the effectiveness of the methodology proposed, production simulation is used in this study to generate some test data. According to experimental results, the accuracy of estimating the remaining cycle time could be improved by up to 64 per cent with the proposed methodology.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Toly Chen ◽  
Yi-Chi Wang
Keyword(s):  
Cycle Time ◽  
Empirical Relationship ◽  
Principal Component ◽  
Time Estimation ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Estimation Performance ◽  
Job Classification ◽  
Fuzzy Neural ◽  
Propagation Network

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.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

1995 ◽  
Vol 3 (3) ◽  
pp. 133-142 ◽  
Author(s):  
M. Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M. White ◽  
D.R. Bahler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Near Infrared ◽  
Back Propagation ◽  
Linear Network ◽  
Data Set ◽  
Input Layer ◽  
Propagation Network ◽  
Better Than

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

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