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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Deep learning driven silicon wafer defect segmentation and classification.

Rohan Ingle¹, Aniket K Shahade¹, Mayur Gaikwad¹

¹Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India.

|January 29, 2025

Summary

This summary is machine-generated.

Automated defect detection on silicon wafers using deep learning significantly improves integrated circuit quality. This study achieved precise defect segmentation and classification, streamlining the manufacturing process.

Keywords:

Deep learning Defect segmentation Image segmentation Integrated circuit Quality management Silicon wafers Wafer Defect Segmentation and classification using Deep Learning Wafer defects

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Area of Science:

Semiconductor manufacturing
Artificial Intelligence in Quality Control

Background:

Integrated circuits (ICs) rely on silicon wafers, which are susceptible to defects during processing.
Manual defect detection is labor-intensive, time-consuming, and hinders efficient quality control.

Purpose of the Study:

To develop an automated system for defect segmentation and classification on silicon wafers using deep learning.
To integrate a Large Language Model (LLM) for interactive defect analysis and guidance.

Main Methods:

Implemented deep learning models for automated defect segmentation and classification.
Utilized metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Dice Index (DSC), Intersection over Union (IoU), Accuracy, Precision, Recall, and F1 Score.
Integrated a Large Language Model (LLM) for a Q&A interface to address defect-related queries.

Main Results:

The segmentation model achieved an MAE of 0.0036, RMSE of 0.0576, DSC of 0.7731, and IoU of 0.6590.
The classification model demonstrated high performance with 0.9705 Accuracy, 0.9678 Precision, 0.9705 Recall, and 0.9676 F1 Score.
Successful identification of high-intensity defect regions post-processing.

Conclusions:

Deep learning effectively automates defect segmentation and classification in IC manufacturing.
The integrated LLM enhances user interaction and provides valuable defect analysis.
This automated approach significantly improves end-product quality and manufacturing efficiency.