Our work presents a robust solution to automatically handle both numerical and categorical production related data without prior knowledge of the low yield root cause. This framework is able to predict FT yield at wafer fabrication stage, so that FT low yield problems can be caught at an earlier production stage compared to past studies. This paper introduces a novel and scalable framework for semiconductor manufacturing Final Test (FT) yield prediction leveraging machine learning techniques. In particular, yield prediction tools, which fully utilize production data, help to improve operational efficiency and reduce production costs. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.Īdvanced data analysis tools and techniques are important for semiconductor companies to gain competitive advantage. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. Process shifts caused by parts and modules aging over time are main processes of failure cause. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment.

Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC.

The purpose of investigation is fault detection and classification (FDC). In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. A single misprocess could jeopardize the whole manufacturing process. Semiconductor manufacturing comprises hundreds of consecutive unit processes. We also used the OES data to generate features related to electron temperature and found that using the electron temperature features together with equipment status variable identification data (SVID) and OES data improved the prediction accuracy of process/equipment fault detection by a maximum of 0.84%. Under a controlled experimental setup of arbitrarily induced fault scenarios, the extended isolation forest (EIF) approach was used to detect anomalies in OES data compared with the conventional isolation forest method in terms of accuracy and speed. In this work, we propose improved stability and accuracy of process fault detection using optical emission spectroscopy (OES) data. Process faults can be caused by abnormal equipment conditions, and the performance drifts of the parts or components of complicated semiconductor fabrication equipment are some of the most unnoticed factors that eventually change the plasma conditions.

To minimize wafer yield losses by misprocessing during semiconductor manufacturing, faster and more accurate fault detection during the plasma process are desired to increase production yields. SMOTE-TOMEK, which removes multiple classes and makes the boundary clear, is suitable for FDC to classify minute changes in plasma-based semiconductor equipment data. In order to improve the FC performance of plasma-based semiconductor process data, it was confirmed that the SMOTE-based model using an undersampling technique such as Tomek link is effective. We compare existing oversampling models to reduce class imbalance, and then we suggest an appropriate sampling strategy. In this study, we suggest a suitable preprocessing method to address the issue of class imbalance in semiconductor process data. Overfitting can occur in machine learning due to the diversity and imbalance of datasets for normal and abnormal. However, class imbalance in semiconductor process data poses a significant obstacle to the introduction of FDC into semiconductor equipment. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences.

Sa pling mo