A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz, Pedro; Godina, Radu; Rodrigues, Eduardo M. G.

doi:10.3390/pr9020305

Cited by 45 publications

(23 citation statements)

References 148 publications

(140 reference statements)

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“…Feature selection technique is a data preprocessing method for data mining processes, especially in dealing with high dimensional machine learning frameworks. Its goal is to build a simple and easy-to-understand model that can be widely used in statistical analysis, machine learning [33][34][35], and data mining [36][37][38]. The selection of important and suitable identification features can not only simplify the calculation but also allow understand the causal relationship, which is a critical part of machine learning.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Chen

Lin

et al. 2021

Mathematics

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In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

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Section: Feature Selection Methodsmentioning

confidence: 99%

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Chen

Lin

et al. 2021

Mathematics

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“…Industry 4.0 gives an idea about smart manufacturing 12 in which foundries are looking to artificial intelligence 13 and the Internet of Things for reducing the cost and maintaining state-of-the-art fabrication quality. 14 Depending on the different capacitance present in MOS capacitors (MOSCAPs), C – V is divided into three major sections: accumulation, depletion, and inversion. 8 The total capacitance per area ( C ) is expressed as the series combination of surface differential capacitance per unit area ( C s ) and oxide capacitance per unit area ( C ox ).…”

Section: Introductionmentioning

confidence: 99%

Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning

et al. 2022

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It is always highly desired to have a well-defined relationship between the chemistry in semiconductor processing and the device characteristics. With the shrinkage of technology nodes in the semiconductors roadmap, it becomes more complicated to understand the relation between the device electrical characteristics and the process parameters such as oxidation and wafer cleaning procedures. In this work, we use a novel machine learning approach, i.e., physics-assisted multitask and transfer learning, to construct a relationship between the process conditions and the device capacitance voltage curves. While conventional semiconductor processes and device modeling are based on a physical model, recently, the machine learning-based approach or hybrid approaches have drawn significant attention. In general, a huge amount of data is required to train a machine learning model. Since producing data in the semiconductor industry is not an easy task, physics-assisted artificial intelligence has become an obvious choice to resolve these issues. The predicted C – V uses the hybridization of physics, and machine learning provides improvement while the coefficient of determination ( R 2 ) is 0.9442 for semisupervised multitask learning (SS-MTL) and 0.9253 for transfer learning (TL), referenced to 0.6108 in the pure machine learning model using multilayer perceptrons. The machine learning architecture used in this work is capable of handling data sparsity and promotes the usage of advanced algorithms to model the relationship between complex chemical reactions in semiconductor manufacturing and actual device characteristics. The code is available at .

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“…A real-time monitoring and diagnosis system that uses a large amount of data is crucial for preventing any shift in the final chip performance or chip failure by a faulty process step. Moreover, research on smart manufacturing, dealing with big data in various fields such as quality control, maintenance, and scheduling, is actively being conducted [5][6][7]. The data generated from the manufacturing process and process control operations to metrology and inspection contain complicated information in fragmented sources.…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

et al. 2021

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Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

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A Review of Data Mining Applications in Semiconductor Manufacturing

Cited by 45 publications

References 148 publications

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

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