Using Artificial Intelligence in IC Substrate Production Predicting

Abstract

Today's technology products are changing with each day, the purpose is to bring more convenience to people, but also the competition among the technology industries is more competitive. In such environment, whether the company's decision-making is correct or not will directly affect the future development of an enterprise. Therefore, how an enterprise can formulate and construct a set of appropriate decision-making systems to accurately predict the future market will be the first important issue for enterprises. This research proposed an artificial intelligence predicting system to estimate manufacturing capacities and client demands, and providing it to manufacturing managers as a reference for inventory arrangements so that inventory can be adjusted appropriately to avoid excessive inventory levels. In recent years, neural networks have been widely and effectively applied to many predicting problems. The main reason is that most of the predicting problems are nonlinear models. And the backward neural network has the ability to construct nonlinear models. In this study, a predicting model combining grey correlation and neural network will be used to establish a high-accuracy predition system for the production predict of IC product. First, grey correlation analysis will be used to screen out the most relevant factors among many factors. And then put these factors into the neural network prediction model for training and prediction. The results show that the training prediction error and the empirical error value are about 14%. This value indicates that the prediction ability is better, so the proposed prediction model can be applied to the prediction of IC substrate production. It provided a predictive reference material and provide decision making with a more accurate, convenient and a fast tool to enhance the company’s competitiveness.