Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole process. In current environments, data monitoring equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during process that affect final chip performance quality. The purpose investigation is fault detection classification (FDC). Various methods, such as statistical or mining methods with machine learning algorithms, have been employed for FDC. this paper, we propose an artificial immune system (AIS), which a biologically inspired computing algorithm, FDC regarding semiconductor equipment. Process shifts caused by parts modules aging over time main processes failure cause. We employ state variable identification (SVID) data, contain operating optical emission spectroscopy (OES) represent plasma information obtained from faulty scenario intentional modification gas flow rate in fabrication achieved modeling prediction accuracy 94.69% selected SVID OES 93.68% alone. To conclude, possibility using AIS field decision making proposed.