基於多尺度注意力、可變形卷積與深度可分離卷積的晶圓缺陷辨識模型

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姓名

王兆玄(Jhao-Syuan Wang) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

基於多尺度注意力、可變形卷積與深度可分離卷積的晶圓缺陷辨識模型
(An Integrated Model for Wafer Defect Pattern Recognition Based on Multi-Scale Attention ,Deformable Convolution and Depth-wise Separable Convolution)

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摘要(中)

本研究針對半導體製造中晶圓缺陷檢測這一關鍵問題展開探討。隨著電路積
體密度的增加和晶圓設計複雜性的增加，晶圓缺陷變得更加普遍。每個晶圓缺陷的發生都是源於某些製造流程的特定異常行為，一套識別晶圓缺陷的系統，有助於發現半導體製造中的異常製程並採取相應措施加以解決。在半導體製造流程中，準確檢測並識別晶圓上的各種缺陷至關重要，但傳統基於卷積神經網絡（CNN）的方法存在一些固有的缺陷，例如高計算量、過擬合和對特定型態缺陷的處理不足。因此，本研究旨在提出一種新型的卷積神經網絡，將深度可分離卷積、多尺度注意力和可變形卷積等技術結合，以提升模型效率、改善缺陷處理能力並增強模型泛化能力。透過這些方法的綜合應用，我們期望能夠有效解決晶圓缺陷檢測中的挑戰，提高檢測準確性和效率，從而促進半導體製造流程的持續優化與提升。

摘要(英)

This study focuses on the crucial issue of wafer defect detection in semiconductor manufacturing. With the increase in circuit integration density and the complexity of wafer design, wafer defects have become more prevalent. Each wafer defect originates from specific abnormal behaviors in the manufacturing process. Accurate detection and identification of various defects on wafers are essential in semiconductor manufacturing. However, traditional convolutional neural network (CNN)-based methods suffer from inherent drawbacks such as high computational complexity, overfitting, and inadequate handling of specific types of defects. Therefore, this study aims to propose a novel convolutional neural network that integrates techniques such as depth-wise separable convolution, multi-scale attention, and deformable convolution to improve model efficiency, enhance defect processing capabilities, and strengthen model generalization ability. Through the comprehensive application of these methods, we expect to effectively address the challenges in wafer defect detection, enhance detection accuracy and efficiency, and promote continuous optimization and improvement of semiconductor manufacturing processes.

關鍵字(中)

★ 晶圓缺陷辨識
★ 卷積神經網路
★ 注意力機制

關鍵字(英)

★ wafer defect recognition
★ multi-scale attention
★ convolution neural network

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究挑戰 3
1.3 研究目的 3
1.4 研究方法 4
第二章文獻回顧 5
2.1 單一類別缺陷模型辨識 5
2.2 卷積神經網路(Convolution Neural Network) 6
2.3 深度可分離卷積(Depthwise Separable Convolutions) 7
2.4 可變形卷積(Deformable Convolutions) 8
2.5 多尺度注意力機制(Multi-Scale Attention) 10
第三章方法論 13
3.1 資料增強 14
3.2 新型卷積神經網路架構 14
3..2.1 深度可分離卷積 14
3.2.2 可變形卷積模組 16
3.2.3 融合深度可分離卷積與可變型卷積的架構 16
3.3 多尺度注意力模塊 18
3.3.1 多尺度特徵融合 18
3.3.2 通道注意力 19
3.3.3 空間注意力 21
第四章實驗結果 23
4.1 數據集 23
4.2 實驗設置 24
4.3 評估指標 24
4.4 模型性能評估 25
4.4.1 平衡和不平衡資料集的效能評估 25
4.4.2 不同分類模型的缺陷分類結果比較 27
4.5 參數量與訓練時間 28
第五章結論與未來研究方向 29
參考文獻 30

參考文獻

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指導教授

葉英傑(Ying-Chieh Yeh)

審核日期

2024-7-16

推文