基於輕量級語義分割網路結合自動生成像素級標籤技術的晶圓圖混合型缺陷模式識別;Wafer Map Mixed-Type Defect Pattern Recognition based on Lightweight Semantic Segmentation Network with Automatic Pixel-Level Label Generation Technique

NCUIR > School of Management at National Central University > Graduate Institute of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/94575

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/94575

Title:	基於輕量級語義分割網路結合自動生成像素級標籤技術的晶圓圖混合型缺陷模式識別;Wafer Map Mixed-Type Defect Pattern Recognition based on Lightweight Semantic Segmentation Network with Automatic Pixel-Level Label Generation Technique
Authors:	洪庭幃;Hong, Ting-Wei
Contributors:	工業管理研究所
Keywords:	晶圓缺陷辨識;語意分割;資料生成;wafer defect recognition;semantic segmentation;data generation
Date:	2024-07-23
Issue Date:	2024-10-09 15:17:15 (UTC+8)
Publisher:	國立中央大學
Abstract:	晶圓製程包含數百個複雜步驟，完成後需進行晶片測試。識別晶圓圖中的缺陷模式有助於找出缺陷原因並優化製程，例如CMP可能導致中心、刮痕、邊緣等缺陷。迅速準確地辨識缺陷模式對提高產量至關重要。而近期在晶圓圖缺陷模式識別領域應用深度學習的研究大大加速了缺陷檢測的過程。然而當不同的缺陷混合在同一塊晶圓上時，混合型晶圓缺陷相較單類別晶圓缺陷複雜，對於晶圓缺陷模式的識別非常困難，而使用語意分割可以有效的辨識混合晶圓缺陷，但語意分割的訓練資料要求像素級晶圓圖標籤。故在本文中，我們提出了一個自動晶圓圖標籤生成技術，並通過使用語義分割方法在晶圓圖上分割不同的缺陷模式。;The wafer fabrication process involves hundreds of complex steps, followed by chip testing upon completion. Identifying defect patterns in wafer maps helps identify the causes of defects and optimize the process. For example, Chemical Mechanical Polishing (CMP) may lead to defects such as center defects, scratches, and edge defects due to particle aggregation or pad hardening during the CMP process. Rapid and accurate identification of defect patterns is crucial for improving yield. Recent research applying deep learning to defect pattern recognition in wafer maps has significantly accelerated the defect detection process. However, when different defects are mixed on the same wafer, mixed-type wafer defects are more complex compared to single-type defects, making defect pattern recognition challenging. Semantic segmentation can effectively identify mixed wafer defects, but training data for semantic segmentation requires pixel-level wafer map labels. Therefore, in this study, we propose an automatic wafer map labeling technique and segment different defect patterns on wafer maps using semantic segmentation.
Appears in Collections:	[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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