A Voting Ensemble Classifier for Wafer Map Defect Patterns Identification in Semiconductor Manufacturing

Saqlain, Muhammad; Jargalsaikhan, Bilguun; Lee, Jong Yun

doi:10.1109/tsm.2019.2904306

Cited by 168 publications

(77 citation statements)

References 36 publications

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“…[11][12][13][14] Normally, machine learning models are evaluated based on different performance measures such as AUC, precision, recall, accuracy, and F-score. 35 Using the test data set, we compared the performances in the mortality prediction models in accordance with AUC, precision, recall, accuracy, and F-score, as described in Table 7. In the experimental results, the AUC values were GBM (0.898), DNN (0.898), RF (0.883), GLM (0.873), and GRACE (0.810), and AUCs in models GBM and DNN were the same.…”

Section: Discussionmentioning

confidence: 99%

A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome

Sherazi

Jeong

Jae

et al. 2019

Health Informatics J

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Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning–based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005–30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning–based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning–based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients.

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Section: Discussionmentioning

confidence: 99%

A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome

Sherazi

Jeong

Jae

et al. 2019

Health Informatics J

Self Cite

View full text Add to dashboard Cite

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“…In [30], prediction of RUL has been researched for wind turbine drivetrain gearboxes where a particle filtering algorithm is introduced that employs a neuro-fuzzy inference system to model state transition and a multinomial resampling method to tackle particle impoverishment. The authors of [17] propose an ensemble classifier using density, geometry, and radon-based features and combining several classification algorithms to identify defect-related wafer map patterns.…”

Section: Predictive Algorithms In Industrial Processesmentioning

confidence: 99%

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

Simulation Modelling Practice and Theory

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The prediction of upcoming events in industrial processes has been a longstanding research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually prevention of defects. While existing approaches have accomplished substantial progress, they are mostly limited to processing of one dimensional signals or require parameter tuning to model environmental parameters. In this paper, we propose an alternative approach based on deep neural networks that simulates changes in the 3D structure of a monitored object in a batch based on previous 3D measurements. In particular, we propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters and predict upcoming events related to sub-optimal performance. We validate our framework on a microelectronics use-case using the recently published "PCB scans" dataset where we simulate changes on the shape and volume of glue deposited on an Liquid Crystal Polymer (LCP) substrate before the attachment of integrated circuits (IC). Experimental evaluation examines the impact of different choices in the cost function during training and shows that the proposed method can be efficiently used for defect prediction.

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“…Piao et al proposed a decision tree ensemble–based approach to aggregate the discrimination power for different feature types. Saqlain et al combined the results of four machine learning classifiers with a soft voting ensemble (SVE) technique, which outperformed regular machine learning‐based classifiers.…”

Section: Introductionmentioning

confidence: 99%

Defect pattern recognition on wafers using convolutional neural networks

Wang

Chen

2020

Quality & Reliability Eng

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In semiconductor manufacturing, wafer testing is performed to ensure the performance of each product after wafer fabrication. The wafer map is used to visualize the color-coded wafer test results based on the locations. The defects on the wafer map may be randomly distributed or form clustered patterns. The various clustered defect patterns are usually caused by assignable faults. The identification of the patterns is thus important to provide valuable hints for the root causes diagnosis. Solving the problems helps improve the manufacturing processes and reduce costs. In this study, we present a novel convolutional neural network (CNN)-based method to automatically recognize the defect pattern on wafer maps. Our method uses polar mapping before the training of CNN to transform the circular wafer map into a matrix, which can be processed within CNN architecture. This procedure also reduces the input size and solves variations in wafer sizes and die sizes. To eliminate the effects of rotation, we apply data augmentation in the training of CNN. Experiments using the real-world dataset prove the effectiveness and superiority of our method.

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A Voting Ensemble Classifier for Wafer Map Defect Patterns Identification in Semiconductor Manufacturing

Cited by 168 publications

References 36 publications

A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome

A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Defect pattern recognition on wafers using convolutional neural networks

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