Key Parameter Identification and Defective Wafer Detection of Semiconductor Manufacturing Processes Using Image Processing Techniques

Fan, Shu-Kai S.; Tsai, Du‐Ming; He, Fei; Huang, Jui-Yu; Jen, C.-L.

doi:10.1109/tsm.2019.2929765

Cited by 20 publications

(13 citation statements)

References 5 publications

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“…Lee et al 55 proposed a systematic data-driven approach for selecting the critical semiconductor steps with considering conventional data properties, that is, missing values, data outliers, random sampling, and class imbalance. Fan et al 56 considered image processing techniques to identify key parameters using the semiconductor trace data. Liu 57 proposed new minority oversampling methods for heterogeneous imbalanced data in fault detection.…”

Section: Data Preprocessing Methodsmentioning

confidence: 99%

A novel hybrid resampling for semiconductor wafer defect bin classification

Park

Pan

Montgomery

2022

Quality & Reliability Eng

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Generally, defective dies on semiconductor wafer maps tend to form spatial clusters in distinguishable patterns which contain crucial information on specific problems of equipment or process, thus it is highly important to identify and classify diverse defect patterns accurately. However, in practice, there exists a serious class imbalance problem, that is, the number of the defective dies on semiconductor wafer maps is usually much smaller than that of the non-defective dies. In various machine learning applications, a typical classification algorithm is, however, developed under the assumption that the number of instances for each class is nearly balanced. If the conventional classification algorithm is applied to a class imbalanced dataset, it may lead to incorrect classification results and degrade the reliability of the classification algorithm. In this research, we consider the semiconductor wafer defect bin data combined with wafer warpage information and propose a new hybrid resampling algorithm to improve performance of classifiers. From the experimental analysis, we show that the proposed algorithm provides better classification performance compared to other data preprocessing methods regardless of classification models.

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Section: Data Preprocessing Methodsmentioning

confidence: 99%

A novel hybrid resampling for semiconductor wafer defect bin classification

Park

Pan

Montgomery

2022

Quality & Reliability Eng

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“…Kim et al (2019) proposed a self-attentive CNN that not only detects faults but also pinpoints time correlated with the faults. Fan et al (2019) represented the sensor stream signals into grayscale images to apply image processing techniques.…”

Section: Predictive Approaches For Fault Detection and Classificationmentioning

confidence: 99%

A sensor data mining process for identifying root causes associated with low yield in semiconductor manufacturing

Kim

Cho

et al. 2023

DTA

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PurposeThe purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment.Design/methodology/approachThis paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis. The process consists of sequential steps: problem definition, data preparation, excursion time and critical sensor identification, data visualization and root cause identification.FindingsA case study is conducted using real-world data collected from a semiconductor manufacturer in South Korea to demonstrate the effectiveness of the diagnosis process. The proposed model successfully identified the excursion time and critical sensors previously identified by domain engineers using costly manual examination.Originality/valueThe proposed procedure helps domain engineers narrow down the excursion time and critical sensors from the massive sensor data. The procedure's outcome is highly interpretable, informative and easy to visualize.

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“…EMD is applicable to nonlinear and unstable data, such as sound frequency signals. EMD utilizes characteristic time scales in signals to define the vibrational modes [8]. A non-zero mean signal can also be used, and the decomposition procedure is called the sifting process.…”

Section: Decomposition Of Sound Frequency Signalsmentioning

confidence: 99%

“…Visualization tools enable practitioners to transform every element of the data into interactive charts and pictures. Fan et al utilized the texture analysis technique with 2-D Fourier transform to analyze images of the critical parameters for detecting defective wafers [8].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases

Jen

Wang

2021

Processes

Self Cite

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Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring; thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of over 90% was realized. In another case study, the proposed method was applied to analyze electrocardiography signals and was similarly able to identify abnormal situations. Thus, the proposed method can be applied to real-time process monitoring and the detection of abnormalities with high accuracy in various situations.

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Key Parameter Identification and Defective Wafer Detection of Semiconductor Manufacturing Processes Using Image Processing Techniques

Cited by 20 publications

References 5 publications

A novel hybrid resampling for semiconductor wafer defect bin classification

A novel hybrid resampling for semiconductor wafer defect bin classification

A sensor data mining process for identifying root causes associated with low yield in semiconductor manufacturing

Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases

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