Algorithm

Process (computing)

Engineering

Fault detection and isolation

Reliability engineering

Support vector machine

Actuator

Operating system

Data mining

Computer science

Fault (geology)

Seismology

Geology

Boosting (machine learning)

Mathematics

Wafer

Artificial intelligence

Semiconductor device fabrication

Machine learning

Electrical engineering

Preprocessor

IEEE Transactions on Semiconductor Manufacturing

In the semiconductor manufacturing, which consists of significantly precise and diverse unit processes, minute defects can cause significantly large risk, which is directly related to the yield. Through fault detection and classification (FDC), the equipment status is monitored, and the potential causes of faults can be investigated. In the mass production process, unbalanced data problems are also important, including preprocessing methods for data analysis in real time. This study proposes a stepwise FDC method with a process fault detection (FD) and faulty equipment part classification. Fault detection (FD) is proposed using a one-class support vector machine (OC-SVM) to determine anomalies that occur during a process, and fault classification (FC) is followed by the importance between variables that determine whether a fault exists is extracted using extreme gradient boosting (XGBoost). Variables whose importance has been confirmed, are reclassified to a part-level based on the variable name, and defects are notified to the part-level level. An empirical study to validate the proposed data-based framework for fault detection and diagnosis was performed under the scenario of unexpected failure of two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {\mathrm {SF}}}_{\mathbf {6}}/{\mathbf {\mathrm {O}}}_{\mathbf {2}}$ </tex-math></inline-formula> mass flow controllers (MFCs). The experimental results confirmed that the application-oriented proposed framework performed well in FDC operations and showed that it can provide part-level notification to engineers.

Machine Learning-Based Process-Level Fault Detection and Part-Level Fault Classification in Semiconductor Etch Equipment