Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

Abstract

Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. 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. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. 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. 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. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

“…The target process was silicon etching using SF 6 , O 2 , and Ar gas plasma. Previous studies have shown that small changes in plasma conditions can significantly affect the Si trench etching results for high aspect ratio [19,21,24,25]. Those studies used 20 × 20 mm 2 silicon pattern samples with an SiO 2 etch mask grown on a 300 mm silicon wafer, whereas in this study, 300 mm silicon wafers were used without a mask.…”

Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment

“…A similar work has been suggested for plasma etch modeling employing a neural network with partial diagnostic data [13]. Recent works on plasma process/equipment FDC are still being actively investigated owing to the advent of machine learning (ML) and artificial intelligence (AI) [14][15][16][17][18].…”

Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data

“…It is commonly used for detecting the etching process endpoint and has been actively investigated for fault detection of plasma equipment using statistics-based modeling approaches [ 12 , 13 ]. A high-performance model can be implemented by manipulating OES data to obtain plasma information, including electron temperature and electron density [ 14 , 15 ], or by selecting radical peaks related to the process based on domain knowledge [ 16 ].…”