Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

The hydroxyapatite(HAp) for the present study was prepared with the wastewater sludge from semiconductor fabrication process and it was crystallized in an electric furnace for 30 min at 900℃. The adsorption characteristics of HAp for phosphate ion in aqueous solution has been investigated. The adsorbed ratio of phosphate ion for HAp were investigated according to the reaction time, amount of HAp, concentration of standard solution, pH of solution, and influence of concemitant ions. The amount of adsorbed phosphate ion decreased with the increase of pH due to the mutual electrostatic repulsion between adsorbed phosphate ions and competitive adsorption between phosphate ion and OH- ion in aqueous solution. The maxium amount of the adsorption equilibrium for phosphate ion was about 24 mg/g of HAp. The HAp would likely to be a possible adsorbent for the removal of phosphate ion in the waste water.