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Updated: Aug 27, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

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Published on: May 28, 2016

A voting-based ensemble feature network for semiconductor wafer defect classification.

Sampa Misra¹, Donggyu Kim¹, Jongbeom Kim¹

¹Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea.

Scientific Reports

|September 28, 2022

Summary

This summary is machine-generated.

Semiconductor wafer defect classification is improved by a novel deep ensemble feature framework (DEFF). This method efficiently analyzes defect patterns, reducing costs and enhancing inspection quality for semiconductor manufacturing.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Materials Science
Computer Science
Electrical Engineering

Background:

Semiconductor wafer defects significantly impact product development and manufacturing yields.
Current machine learning (ML)-based automatic defect classification (ADC) methods are often inefficient and resource-intensive due to reliance on numerous image recognition features.

Purpose of the Study:

To propose a novel deep ensemble feature framework (DEFF) for automatic defect classification (ADC) of semiconductor wafer surfaces.
To enhance the efficiency and accuracy of wafer defect inspection by reducing reliance on conventional ML feature engineering.

Main Methods:

Developed a deep ensemble feature framework (DEFF) comprising an ensemble feature network and a decision network layer.
Utilized multiple pre-trained convolutional neural network (CNN) models within the feature network to learn defect representations.
Computed ensemble features by concatenating learned features and employed a voting-based ensemble learning strategy for final classification.

Main Results:

The proposed DEFF technique effectively classifies various types of semiconductor wafer surface defects.
Achieved enhanced classification performance through the combination of deep ensemble features and ensemble learning strategies.
Demonstrated the method's efficacy using real-world data from SK Hynix.

Conclusions:

The DEFF approach offers a more cost-effective and efficient solution for automatic defect classification in semiconductor manufacturing.
This framework improves upon conventional ML-based ADC methods by automating feature extraction and leveraging ensemble learning.
The study validates the practical applicability and performance of the proposed DEFF strategy in industrial settings.