Abstract
Abstract CMOS image sensor (Complementary Metal-Oxide Semiconductor Image Sensor, CIS) is technology driven and highly capital -intensive industry. Hence the way to maximize the utilization of production equipment becomes the stepping stone for business to minimize its cost of equipment and manufacturing, also to create competitive advantage. For production testing of CMOS image sensors, Chroma testing machine with the power supply unit is the most critical tool among all. In order to maintain normal operations and ensure the output volume of the machine, currently the production line is still dependent on the experience of senior maintenance staff. However due to many reasons, high turnover rate of senior maintenance staff results in difficulty of obtaining effective accumulation the knowledge, skills and experience. Through scholars used of neural networks in the success experience of semiconductor machine failure analysis and combined with the experience machine production line maintenance personnel, the relevant test data and neural network (Artificial Neural Network, ANN) technology for fault classification and analysis, constructing a CMOS image sensor production test machine fault diagnosis mode. Back Propagation Neural Network (BPN) is used to identify the relationship between symptom and cause of the fault. Model efficiency is measured by the network performance. The results show that the proposed model for the diagnostics of CMOS image sensor test tool malfunction obtained good results, so this model should be applied to related fields of test equipment. Key word: Neural Network、Cost Of Manufacturing、Network Performance