A neural network forecasting model for consumable parts in semiconductor manufacturing

Abstract

Purpose

The purpose of this paper is to create a usable life forecast model for consumable parts using neural network approach. It focuses on a consumable probe card used in the semiconductor wafer testing operation. Referring to the relevant resources and the semiconductor testing operation, a fundamental concept is built to develop a probe card management system.

Design/methodology/approach

A neural network analysis software package, Q‐net2000, is applied in this study. In this case, there is one hidden layer and the neural network learning rates and momentum are set to 0.1 and 0.7. Forecast the usable life by inputting the initial values of the neural network variables into a back‐propagation neural network.

Findings

In this system, the first thing is to collect the production, maintenance and repair data, and then analyze those data by using a neural network methodology to effectively forecast a probe card's usable life. Those data are integrated to derive an optimum timing of placing a probe card order using an inventory control technique. Finally, the actual production data of a company are used to verify the feasibility of this research.

Research limitations/implications

The results presented are based on a representative expendable probe card manufacturing process in the Taiwan industry, a range of alternative scenarios and changes to the process design can be investigated using the simulation model.

Practical implications

For the semiconductor industry, the research supports the introduction on lifecycle forecast technology for expendable probe card manufacturing process.

Originality/value

The paper proposes a neural network forecast analysis to solve the case company's current management problem of determining the life cycle of probe cards in an earlier time.

Citation

Jou, Y., Wee, H., Chen, H., Hsieh, Y. and Wang, L. (2009), "A neural network forecasting model for consumable parts in semiconductor manufacturing", Journal of Manufacturing Technology Management, Vol. 20 No. 3, pp. 404-412. https://doi.org/10.1108/17410380910936828