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

Che-Jung Chang,Wen-Li Dai,Der-Chiang Li,Chien-Chih Chen

doi:10.1155/2013/526806

Abstract

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.

Highlights

Firms can gain competitive advantages by more effectively controlling their manufacturing systems [1]
The early stages of a manufacturing system are especially important for managers, because any slight change in the parameters used at this time will significantly influence final product quality and manufacturing performance [2]
193.264 193.640 194.414 194.731 194.637 194.505 195.798 196.284 196.169 between the actual and forecast results with regard to the geometric shape, and this explains that the proposed approach can properly reflect the data trend, with no serious inconsistencies between the forecast results and the real situation. Both the measurements of ratio of standard deviation (RSD) and probability of small error (PSE) are at an acceptable level, so there are neither extreme values nor a high level of error discreteness, which both indirectly confirm the stability of the AGM(1,1) model

Summary

Introduction

Firms can gain competitive advantages by more effectively controlling their manufacturing systems [1] In this context, the early stages of a manufacturing system are especially important for managers, because any slight change in the parameters used at this time will significantly influence final product quality and manufacturing performance [2]. The introductory stage of the wafer-level packaging (WLP) process is an example of a small dataset problem. The WLP process is a new, advanced technology for the packaging industry, and manufacturers do not have much experience to draw its form when seeking to improve it. An appropriate forecasting tool that can deal with incomplete information based on small datasets is required for effective management of the WLP process. There are five quality inspections in the WLP process to ensure the process standards, and these examine the passivation layer defects, diameter size, coplanarity, metal bump defects, and the height of metal bumps

Objectives

Methods

Results

Conclusion