scholarly journals Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Che-Jung Chang ◽  
Der-Chiang Li ◽  
Wen-Li Dai ◽  
Chien-Chih Chen
Keyword(s):  
Semiconductor Manufacturing ◽  
Manufacturing System ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey System ◽  
Wafer Level ◽  
Short Term ◽  
Packaging Process ◽  
Wafer Level Packaging

The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1) grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.

Application of Grey Model Theory to Forecast Flight Training Time

2014 ◽  
Vol 635-637 ◽  
pp. 1696-1699
Author(s):  
Jun Wen ◽  
Hai Feng Duan ◽  
Shu Xia Sun ◽  
Ming Xing Li ◽  
Jian Fei Lv
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Grey Model ◽  
Flight Training ◽  
Civil Aviation ◽  
Grey System ◽  
Short Term ◽  
Training Time ◽  
Forecast Result ◽  
High Prediction

Grey model GM(1,1) is applied to forecast flight training time. The discreteness of originality data is overcome and the high-precise predicted result is received under the condition of a small amount of data. This paper takes a short-term forecast flight training time of Civil Aviation Flight University of China (CAFUC) by using grey system theory. With a comparison of the actual data to the forecast result, it is proved that using grey system theory to forecast the flight training time of Civil Aviation Flight University of China is feasible with relatively high prediction accuracy.

Prediction of Technical Reserves Based on Grey Model — GM(1,1): Evidence from Non-life Egyptian Insurance Market

2019 ◽  
Vol 10 (9) ◽  
pp. 852-860
Author(s):  
Mahmoud Elsayed ◽  
◽  
Amr Soliman ◽  
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Insurance Market ◽  
Grey Model ◽  
Loss Ratio ◽  
Grey System ◽  
Mathematical Technique ◽  
Supervisory Authority ◽  
Error Ratio ◽  
Using Data

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).

Warpage Simulation During Fan-Out Wafer-Level Packaging Process with Uncertainty of Material Properties

2021 ◽  
Vol 21 (5) ◽  
pp. 2987-2991
Author(s):  
Geumtaek Kim ◽  
Daeil Kwon
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Material Properties ◽  
Coefficient Of Thermal Expansion ◽  
Input Output ◽  
Wafer Level ◽  
High Input ◽  
Element Analysis ◽  
Packaging Process ◽  
Wafer Level Packaging

Along with the reduction in semiconductor chip size and enhanced performance of electronic devices, high input/output density is a desired factor in the electronics industry. To satisfy the high input/output density, fan-out wafer-level packaging has attracted significant attention. While fan-out wafer-level packaging has several advantages, such as lower thickness and better thermal resistance, warpage is one of the major challenges of the fan-out wafer-level packaging process to be minimized. There have been many studies investigating the effects of material properties and package design on warpage using finite element analysis. Current warpage simulations using finite element analysis have been routinely conducted with deterministic input parameters, although the parameter values are uncertain from the manufacturing point of view. This assumption may lead to a gap between the simulation and the field results. This paper presents an uncertainty analysis of wafer warpage in fan-out wafer-level packaging by using finite element analysis. Coefficient of thermal expansion of silicon is considered as a parameter with uncertainty. The warpage and the von Mises stress are calculated and compared with and without uncertainty.

Active Device Performance after Fan-out Wafer-level Packaging Process

2018 ◽  
Author(s):  
Hong-Yu Li ◽  
Masaya Kawano ◽  
Simon Lim ◽  
Daniel Ismael Cereno ◽  
Vasarla Nagendra Sekhar
Keyword(s):  
Device Performance ◽  
Wafer Level ◽  
Active Device ◽  
Packaging Process ◽  
Wafer Level Packaging

Low-cost wafer level packaging process

2000 ◽  
Author(s):  
Rahul Kapoor ◽  
Swee Y. Khim ◽  
Goh H. Hwa
Keyword(s):  
Low Cost ◽  
Wafer Level ◽  
Packaging Process ◽  
Wafer Level Packaging

Research and Application of Grey Predictive Model Based on Wavelet Analysis

2012 ◽  
Vol 170-173 ◽  
pp. 2912-2916
Author(s):  
Hai Ping Xiao ◽  
Lan Lan Chen ◽  
Yi Qiang Chen ◽  
Zhong Qun Guo
Keyword(s):  
System Theory ◽  
Wavelet Analysis ◽  
Predictive Model ◽  
Wavelet Transformation ◽  
Scientific Basis ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey System ◽  
Predicting Model ◽  
Feasible Method

It is the scientific basis of instructing the project to produce and operate that the deformation is monitored, and the analysis and prediction in constructing and operating of project is one of the important jobs. In order to analyze and predict the deformation of the project more timely and accurately, the paper analyzed and established the feasibility of wavelet-grey predicting model on the basis of the grey system theory in modeling limitations and the characteristics of wavelet transformation. With the comparison of predictive datas in two kinds of models, the results show, the predictive datas of the wavelet-grey model are more accurately than grey model’s, and has achieved good results in prediction of the engineering, is a feasible method.

scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

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