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Machine learning-based techniques for fault diagnosis in the semiconductor manufacturing process: a comparative study

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Abstract

Industries are going through the fourth industrial revolution (Industry 4.0), where technologies like the Industrial Internet of things, big data analytics, and machine learning (ML) are extensively utilized to improve the productivity and efficiency of manufacturing systems and processes. This work aims to further investigate the applicability and improve the effectiveness of ML prediction models for fault diagnosis in the smart manufacturing process. Hence, we propose several methodologies and ML models for fault diagnosis for smart manufacturing process applications. A case study has been conducted on a real dataset from a semiconductor manufacturing (SECOM) process. However, this dataset contains missing values, noisy features, and class imbalance problem. This imbalance problem makes it so difficult to accurately predict the minority class, due to the majority class size difference. In the literature, efforts have been made to alleviate the class imbalance problem using several synthetic data generation techniques (SDGT) on the UCI machine learning repository SECOM dataset. In this work, to handle the imbalance problem, we employed, compared, and evaluated the feasibility of three SDGT on this dataset. To handle issues related to the missing values and noisy features, we implemented two missing values imputation techniques and feature selection techniques, respectively. We then developed and compared the performance of ten predictive ML models against these proposed methodologies. The results obtained across several evaluation metrics of performance were significant. A comparative analysis shows the feasibility and validate the effectiveness of these SDGT and the proposed methodologies. Some among the proposed methodologies could produce an accuracy in the range of 99.5% to 100%. Furthermore, based on a comparative analysis with similar models from the literature, our proposed models outpaced those proposed in the literature.

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Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

1NNC:

1-Nearest neighbor classifier

AdaBoost:

Adaptive boosting

ADASYN:

Adaptive synthetic oversampling

ANN:

Artificial neural network

AWSMOTE:

Adaptive-weighting SMOTE

BEBS:

Bagging of extrapolation borderline-SMOTE SVM

BSMOTE-SVM:

Borderline-SMOTE-SVM

CV:

Cross-validation

DL:

Deep learning

DT:

Decision tree

FN:

False negative

FP:

False positive

FPR:

False positive rate

FS:

Feature scaling

GBM:

Gradient boosting machine

k-NN:

K-nearest neighbor

k-NNI:

K-NN imputation

LDA:

Linear discriminant analysis

LR:

Logistic regression

MARS:

Multivariate adaptive regression splines

MI:

Mean imputation

ML:

Machine learning

MLP:

Multilayer perceptron

MRD:

Modified raw dataset

MWMOTE:

Majority weighted minority oversampling technique

NB:

Naïve Bayes

PCA:

Principal component analysis

PSO-DBN:

Particle swarm optimization–deep belief network

RF:

Random forest

RFE:

Recursive feature elimination

SDGT:

Synthetic data generation techniques

SECOM:

Semiconductor manufacturing

SELECTFDR:

Estimated false discovery rate

SMOTE:

Synthetic minority oversampling technique

SVM:

Support vector machines

TN:

True negative

TP:

True positive

TPR:

True positive rate

UCI SECOM dataset:

UCI machine learning repository SECOM dataset

UFS:

Univariate feature selection

XGBoost:

Extreme gradient boosted trees

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Authors and Affiliations

  1. Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, Turkey

    Abubakar Abdussalam Nuhu, Qasim Zeeshan & Babak Safaei

  2. Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, Gauteng, 2006, South Africa

    Babak Safaei

  3. Department of Industrial Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

    Muhammad Atif Shahzad

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  1. Abubakar Abdussalam Nuhu
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Correspondence to Babak Safaei.

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Nuhu, A.A., Zeeshan, Q., Safaei, B. et al. Machine learning-based techniques for fault diagnosis in the semiconductor manufacturing process: a comparative study. J Supercomput 79, 2031–2081 (2023). https://doi.org/10.1007/s11227-022-04730-x

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