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Title

Anomaly Detection in Semiconductor Cleanroom Using Isolation Forest

Author

Israt Jahan,

Email

isratjahan.cse@diu.edu.bd

Abstract

Wafer fabrication in semiconductor companies is frequently afflicted by anomalies that can have a negative impact on the cleanroom environment, leading to wafer defects. This study describes an anomaly detection system for edge devices in a semiconductor cleanroom that uses the Isolation Forest method. The multidimensional dataset, which comprises various sizes of ultrafine PM1 particles, is extremely harmful to wafers and can be detected very effectively using Isolation Forest, with an F 1 -score of 0.9899795 and an AUC of 0.99.

Keywords

Cleanroom , anomalies , particulate matter

Journal or Conference Name

International Conference on ICT Convergence

Publication Year

2021

Indexing

scopus