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.

1. 서 론
2. 이론적 배경
    2.1 이상 탐지
    2.2 이진 분류
    2.3 지도 이상 분류
    2.4 준지도 이상 분류
    2.5 XAI(eXplainable AI)
3. 이상 탐지 방법론
    3.1 데이터 수집
    3.2 데이터 전처리
    3.3 특성 선택
    3.4 데이터 불균형 처리
    3.5 모델 적용
    3.6 모델 성능 평가 및 비교
    3.7 이상 발생 원인 탐지
4. 사례적용
    4.1 데이터 전처리
    4.2 특성 선택
    4.3 데이터 불균형 처리
    4.4 모델 적용
    4.5 모델 성능 평가 및 비교
    4.6 이상 발생 원인 탐지
5. 결 론
References

• Kang-Min An(한양대학교 일반대학원 경영컨설팅학과) | 안강민
• Ju-Eun Shin(한양대학교 일반대학원 경영컨설팅학과) | 신주은
• Dong Hyun Baek(한양대학교 경상대학 경영학부) | 백동현 Corresponding Author