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

Nuhu, Abubakar Abdussalam; Zeeshan, Qasim; Safaei, Babak; Shahzad, Muhammad Khurram

doi:10.1007/s11227-022-04730-x

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…It is important to accurately predict the RUL of components so as to maximize its lifetime and also know when it needs repair or replacement to avoid unseen failures. It is observed that artificial neural network is one of the main techniques used in developing predictive models to predict RUL, probably because of its ability to learn patterns and features in data to make accurate predictions [21,30]. The unique feature of TF-IDF technique in extracting terms from document can be utilized to extract important features from data especially when solving classification problems [27].…”

Section: Discussionmentioning

confidence: 99%

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Applications of Machine Learning in Aircraft Maintenance

Karaoğlu¹,

Mbah²,

Zeeshan³

2022

JEMSE

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Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance.

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Section: Discussionmentioning

confidence: 99%

“…Nuhu et al [21] investigated the applicability and effectiveness of ML prediction models for fault diagnosis in smart manufacturing. In predicting the Remaining Useful Lifetime (RUL) of aircraft parts, Azevedo et al [22] developed a web-based application.…”

Section: Recent Researchmentioning

confidence: 99%

Applications of Machine Learning in Aircraft Maintenance

Karaoğlu¹,

Mbah²,

Zeeshan³

2022

JEMSE

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“…This predictive maintenance approach enhanced the semiconductor manufacturing productivity. Nuhu et al introduced synthetic data generation techniques that combined two missing value imputation methods and feature selection techniques [15]. This approach achieved an accuracy ranging from 99.5% to 100% when paired with the proposed machine-learning (ML) methods.…”

Section: Introductionmentioning

confidence: 99%

Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction

Yu,

Yang,

Chuang

et al. 2024

Applied Sciences

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Machine- and deep-learning methods are used for industrial applications in prognostics and health management (PHM) for semiconductor processing and equipment anomaly detection to achieve proactive equipment maintenance and prevent process interruptions or equipment downtime. This study proposes a Pruning Quantized Unsupervised Meta-learning DegradingNet Solution (PQUM-DNS) for the fast training and retraining of new equipment or processes with limited data for anomaly detection and the prediction of various equipment and process conditions. This study utilizes real data from a factory chiller host motor, the Paderborn current and vibration open dataset, and the SECOM semiconductor open dataset to conduct experimental simulations, calculate the average value, and obtain the results. Compared to conventional deep autoencoders, PQUM-DNS reduces the average data volume required for rapid training and retraining by about 75% with similar AUC. The average RMSE of the predictive degradation degree is 0.037 for Holt–Winters, and the model size is reduced by about 60% through pruning and quantization which can be realized by edge devices, such as Raspberry Pi. This makes the proposed PQUM-DNS very suitable for intelligent equipment management and maintenance in industrial applications.

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“…In computer science, ML is used for various tasks, such as natural language processing [1][2][3][4][5], image recognition [6][7][8], or computer vision [9][10][11][12][13][14]. In engineering, ML is applied in the optimization and control of complex systems [15][16][17][18], the prediction of equipment failures [19][20][21], or the enhancement of manufacturing processes [22][23][24][25]. ML is also extensively used in materials science for materials discovery [26][27][28][29][30], property prediction [31][32][33][34], and accelerated materials design [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

X-ray Diffraction Data Analysis by Machine Learning Methods—A Review

Surdu,

Győrgy

2023

Applied Sciences

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X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials.

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

Cited by 10 publications

References 47 publications

Applications of Machine Learning in Aircraft Maintenance

Applications of Machine Learning in Aircraft Maintenance

Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction

X-ray Diffraction Data Analysis by Machine Learning Methods—A Review

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