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

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Contrastive Learning with Global and Local Representation for Mixed-Type Wafer Defect Recognition.

Shantong Yin¹, Yangkun Zhang¹, Rui Wang¹

¹School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This study introduces a self-supervised contrastive learning framework to identify semiconductor wafer defect patterns. The model effectively classifies and segments mixed defects, even with limited labeled data, improving efficiency and quality.

Keywords:

contrastive learning pattern classification and segmentation wafer bin map

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Area of Science:

Semiconductor Manufacturing
Artificial Intelligence
Computer Vision

Background:

Semiconductor wafer bin maps (WBMs) present complex defect patterns crucial for integrated circuit (IC) manufacturing.
Accurate defect classification and segmentation are vital for root cause analysis, cost reduction, and quality enhancement.
Traditional supervised methods struggle with intricate WBMs and require extensive manual labeling.

Purpose of the Study:

To develop a self-supervised learning framework for classifying and segmenting mixed-type WBM defect patterns.
To address the challenges of complex WBMs and the limitations of labor-intensive supervised learning.
To improve the efficiency and accuracy of defect recognition in IC manufacturing.

Main Methods:

A novel self-supervised contrastive learning framework is proposed.
The model integrates a global module for image-level representations and a local module for regional detail comprehension.
This approach enables effective learning from both labeled and unlabeled WBM data.

Main Results:

The proposed framework demonstrates strong performance in classifying and segmenting mixed WBM defect patterns.
Effective defect recognition is achieved even with a limited number of labeled samples.
The model's ability to handle complex, coexisting defect patterns is highlighted.

Conclusions:

Self-supervised contrastive learning offers a viable solution for WBM defect pattern recognition.
The dual-module approach enhances both classification and segmentation capabilities.
This method reduces reliance on large labeled datasets, making it practical for the IC industry.