Electronics product life cycles are becoming shorter and shorter because of the severe global competition. In such highly competitive industry, it has become an important strategy to accelerate new products launching to the market to earn more shares. However, the lead times of pilot runs are usually long in new product development (NPD) processes, and reducing pilot runs has thus become one of the key tasks of manufacturing systems. Specifically, since the shorter a test period is the smaller sample size one can obtain, making that to find a small data learning method for a manufacturing system being a new challenge. Facing the problem, this work, based on the box plots and the fuzzy techniques, develops an approach to systematically generate synthetic samples to help stabilize the learning process for the used back-propagation neural network (BPN). A real learning task taken from the Array process of a TFT-LCD manufacturer (a new high-resolution product of 4K2K in 2013) is employed as an example to illustrate the details of the proposed method. The task contains nine inputs and 72 output manufacturing attributes, but only with 20 samples. It is quite difficult for most existing modeling algorithms to deal with such a high dimensional situation when the sample size is small. The experiment results show that the proposed approach can effectively improve the robustness and preciseness of a BPN forecasting model. In addition to the reduction of pilot runs, more process knowledge is obtained in the input-output analysis.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence