The semiconductor industry deals with the production at a scale of nanometer, thus resulting in the process control with little margin of error. Timely detection of faults during the manufacturing process is critical to the improvement in product yields. Difficulty of detecting accurately faulty processes and products is due to the abundant of data obtained from hundreds of tool-state and process-state sensors. We thus analyze this problem through the computational intelligence techniques. The analysis results reveal the minimal set of features for fault detection as well as the high precision classification model of faults.