Fuzzy tree classification system for fault diagnosis on Ion implanter

Abstract

Ion implanter is critical to modern integrated-circuit (IC) manufacturing. Ion implanter requires a long process due to low acceleration and high concentration process, which results in low productivity and becomes a bottleneck in the semiconductor fabrication. Furthermore, the wafer is not re-workable if existing error operation either caused by the operator or the machine. Therefore, it is important to develop a real-time fault detection system to minimize the probable down time of the ion implanter. In this work, a real-time fault detection system, which is based on the Fuzzy Tree Classification Systems (FTCS), is proposed to monitor the operation of the Eaton NV6200A/AV ion implanter. Two datasets, the 26-recipe and the 42- recipe cases, provided by a renowned wafer foundry in Taiwan were used to test the performance of the proposed FTCS. The datasets were obtained through the SECS-II interface. Test results demonstrate that the proposed FTCS can work real-time for fault detection of the Eaton NV6200A/AV ion implanter.