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Anomaly Detection Using Signal Segmentation and One-Class Classification in Diffusion Process of Semiconductor

Kyuchang Chang¹, Youngji Yoo², Jun-Geol Baek¹

¹Department of Industrial and Management Engineering, Korea University, Seoul 02841, Korea.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces an advanced diagnostic method for semiconductor manufacturing sensor data, enhancing fault detection and classification (FDC) performance. The novel approach improves real-time identification of subtle anomalies, boosting productivity and yield.

Keywords:

anomaly detection fault detection and classification (FDC)isolation forest (iF)local outlier factor (LOF)one-class classification (OCC)signal segmentation

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Area of Science:

Semiconductor Manufacturing
Data Science
Signal Processing

Background:

Real-time fault detection and classification (FDC) is crucial for semiconductor manufacturing yield.
Distinguishing subtle normal from abnormal sensor data is challenging due to data complexity.
Low frequency of abnormal data necessitates models trained primarily on normal data.

Purpose of the Study:

To propose a novel diagnostic method for sensor signals in semiconductor manufacturing.
To enhance fault detection and classification (FDC) performance for improved productivity and yield.
To address the challenges of subtle data differences and low anomaly frequency.

Main Methods:

A two-phase approach involving signal segmentation, feature extraction using Local Outlier Factors (LOF), and one-class classification (OCC) with the Isolation Forest (iF) algorithm.
Phase I: Model development using signal segmentation, LOF feature extraction, and iF-based OCC.
Phase II: Anomaly detection through signal segmentation, feature extraction, and anomaly identification.

Main Results:

The proposed method demonstrates superior performance compared to existing baseline methods.
Effective identification of subtle differences between normal and abnormal sensor data.
Improved FDC performance, directly impacting manufacturing productivity and product yield.

Conclusions:

The developed diagnostic method effectively overcomes challenges in semiconductor FDC.
The technique offers a significant improvement in identifying anomalies in time series data.
This approach enhances real-time quality control and yield prediction in semiconductor fabrication.