• Title/Summary/Keyword: wafer bin map

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Detection of Defect Patterns on Wafer Bin Map Using Fully Convolutional Data Description (FCDD) (FCDD 기반 웨이퍼 빈 맵 상의 결함패턴 탐지)

  • Seung-Jun Jang;Suk Joo Bae
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.46 no.2
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    • pp.1-12
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    • 2023
  • To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

Wafer bin map failure pattern recognition using hierarchical clustering (계층적 군집분석을 이용한 반도체 웨이퍼의 불량 및 불량 패턴 탐지)

  • Jeong, Joowon;Jung, Yoonsuh
    • The Korean Journal of Applied Statistics
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    • v.35 no.3
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    • pp.407-419
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    • 2022
  • The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.

Deep Learning-Based Defect Detection in Cu-Cu Bonding Processes

  • DaBin Na;JiMin Gu;JiMin Park;YunSeok Song;JiHun Moon;Sangyul Ha;SangJeen Hong
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.2
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    • pp.135-142
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    • 2024
  • Cu-Cu bonding, one of the key technologies in advanced packaging, enhances semiconductor chip performance, miniaturization, and energy efficiency by facilitating rapid data transfer and low power consumption. However, the quality of the interface bonding can significantly impact overall bond quality, necessitating strategies to quickly detect and classify in-process defects. This study presents a methodology for detecting defects in wafer junction areas from Scanning Acoustic Microscopy images using a ResNet-50 based deep learning model. Additionally, the use of the defect map is proposed to rapidly inspect and categorize defects occurring during the Cu-Cu bonding process, thereby improving yield and productivity in semiconductor manufacturing.

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