A neural network based information granulation approach to shorten the cellular phone test process

In the cellular phone OEM/ODM industry, reducing test time and cost are crucial due to fierce competition, short product life cycle, and a low margin environment. Among the inspection processes, the radio frequency (RF) function test process requires more operation time than any other. Hence, manufacturers need an effective method to reduce the RF test items so that the inspection time can be reduced while maintaining the quality of the RF function test. However, traditional feature selection methods such as neural networks and genetic algorithm lead to a high level of Type II error in the situation of imbalanced data where the amount of good products is far greater than the defective products. In this study, we propose a neural network based information granulation approach to reduce the RF test items for the finished goods inspection process of a cellular phone. Implementation results show that the RF test items were significantly reduced, and that the inspection accuracy remains very close to that of the original testing process. In addition, the Type II errors decreased as well.