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Light Enhanced Hydrofluoric Acid Passivation: A Sensitive Technique for Detecting Bulk Silicon Defects

Light Enhanced Hydrofluoric Acid Passivation: A Sensitive Technique for Detecting Bulk Silicon Defects

Published on: January 4, 2016

A full-flow inspection method based on machine vision to detect wafer surface defects.

Naigong Yu^1,2,3, Hongzheng Li^1,2,3, Qiao Xu^1,2,3

¹Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Mathematical Biosciences and Engineering : MBE

|July 28, 2023

Summary

This summary is machine-generated.

This study introduces an advanced machine vision method for semiconductor wafer surface defect detection. The novel approach achieves over 97% accuracy in identifying defective dies, improving manufacturing yield.

Keywords:

feature extraction image segmentation machine learning machine vision wafer surface defect detection

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Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

Published on: February 27, 2013

Area of Science:

Materials Science
Computer Science
Electrical Engineering

Background:

Semiconductor manufacturing requires precise wafer surface defect detection for improved yield.
Existing machine learning and digital image processing algorithms face challenges in industrial application, including data acquisition, computational cost, and generalization.
These limitations hinder the widespread adoption of automated wafer inspection systems.

Purpose of the Study:

To develop a robust, full-flow machine vision inspection method for semiconductor wafer surface defect detection.
To address the limitations of current methods, particularly in training sample acquisition, computational demands, and generalization.
To enhance the accuracy and efficiency of wafer defect identification in industrial settings.

Main Methods:

A novel die segmentation algorithm using candidate frame fitting and coordinate interpolation is proposed to accurately segment all dies, preventing missed die splitting.
A die defect anomaly detection method based on regional defect feature clustering is introduced to minimize noise interference during feature extraction.
The integrated approach combines segmentation and defect detection for comprehensive wafer inspection.

Main Results:

The proposed method precisely positions and segments die images on semiconductor wafers.
The inspection system achieves a high accuracy of over 97% in identifying defective dies.
The defect detection method effectively reduces the impact of noise, enhancing detection reliability.

Conclusions:

The developed full-flow inspection method offers a practical solution for industrial wafer surface defect detection.
The approach overcomes key challenges associated with training data, computational load, and generalization.
This machine vision technique significantly enhances the accuracy and applicability of wafer inspection in semiconductor manufacturing.