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Wafer defect recognition method based on multi-scale feature fusion.

Yu Chen¹, Meng Zhao^1,2, Zhenyu Xu³

¹Research Center for Applied Mechanics, School of Electro-Mechanical Engineering, Xidian University, Xi'an, China.

Frontiers in Neuroscience

|June 19, 2023

Summary

This summary is machine-generated.

This study introduces the Multi-Feature Fusion Perceptual Network (MFFP-Net) for automated wafer defect recognition in chip manufacturing. The MFFP-Net achieves 96.71% accuracy, enhancing quality and production yield.

Keywords:

deep learning denoise multi-scale feature recognition wafer defect

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Area of Science:

Materials Science
Computer Science
Electrical Engineering

Background:

Wafer defect recognition is critical in chip manufacturing for identifying and resolving production issues.
Accurate defect identification directly impacts wafer quality and overall production yield.

Purpose of the Study:

To develop a high-precision wafer defect recognition system.
To improve the quality and production yield in the chip manufacturing industry.

Main Methods:

Proposed the Multi-Feature Fusion Perceptual Network (MFFP-Net), inspired by human visual perception.
MFFP-Net processes information at multiple scales and aggregates features for simultaneous abstraction.
A feature fusion module captures fine-grained details and texture information, preventing data loss.

Main Results:

MFFP-Net demonstrated strong generalized ability on the WM-811K dataset.
Achieved state-of-the-art accuracy of 96.71% in wafer defect recognition.
The network effectively identifies diverse defect patterns.

Conclusions:

MFFP-Net offers an effective solution for enhancing wafer quality and production yield.
The proposed method provides a robust approach to automated defect recognition in semiconductor manufacturing.
This advancement supports the chip manufacturing industry in optimizing production processes.