A Review of Data Mining Applications in Semiconductor Manufacturing

Abstract

For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

1. Introduction

The last few decades have seen the birth of a great diversity of products and services associated with electrical and electronic equipment, and witnessed the presence of electronic and electrical equipment in a large number of products and services, which are subject to constant change [1]. During the last few years, since semiconductor manufacturing processes have gradually diminished in size, the number of transistors that can be fabricated on a sole silicon wafer can amount to a billion units [2]. In order to account for the dynamic evolution of production and distribution and the changes caused by technological advances and inventions, companies that operate in this field need to be flexible and to be able to adapt quickly to a constantly changing environment [3].

Semiconductor production is the process that creates integrated circuits, such as transistors, LEDs, or diodes that can be found in electrical devices and consumer electronics. During the front-end process, the crystalline silicon ingot is produced and the wafers are cut, the electrical circuits are created by photolithography and other chemical processes and, finally, they are electronically tested. In the back-end process, the chunks are cut from the wafer, wired (glued), encapsulated, and tested [4]. The semiconductor manufacturing industrial units (known also as fabs) are one of the highest capital-intensive and entirely automated production systems, in which agnate processes and equipment are utilized to manufacture integrated circuits through a wide range of extensive and complex processes with firmly controlled manufacturing processes, reentering process flows, advanced and complex equipment, and demanding deadlines for complying with constantly unpredictable demands of a constantly increasing product mix [5].

The concept Industry 4.0 involves employing artificial intelligence technologies, data mining techniques, big data and deep learning analysis to the current industrial infrastructure for the purpose of developing innovations that are disruptive [6]. The objective is to strive to put into practice this concept, which will allow flexible decision-making and smart manufacturing systems, as anticipated by the Industry 4.0 concept. Therefore, by turning Industry 4.0 a reality, the role of the Internet of Things (IoT) and additional emergent technologies will have a central role [7]. So far, the tendency to have unmanned operations and increasing automation in semiconductor production systems, as in other production technologies, is constantly growing [8].

Conventionally, semiconductor production systems are known for having a highly complex and lengthy manufacturing process. Typically, semiconductor wafers require a number of process steps that could easily surmount half of a thousand to be produced [9,10]. The level of complexity of every step is frequently equated to that of a medium-sized industrial unit, particularly in such areas such as logistics, planning, control, and data volume, among other steps. Consequently, growing requirements and pressure to perform with a high plant productivity pose a difficult challenge for companies operating in semiconductor manufacturing [1].

The ever-growing demand for integrated circuits that are able to deliver higher performances at lower costs is something semiconductor companies are well familiar with. Therefore, wafer metrology tools are employed for designing and producing semiconductors, cautiously monitoring line widths, film properties, and possible defects in order to improve the production process. Data mining techniques together with metrology tools and wafer verification abilities guarantee a close desired result of the electrical and physical properties of produced semiconductors. Data mining with wafer metrology can accurately and quickly recognize surface pattern defects, particles, and additional conditions that are capable of causing adverse effects on semiconductor performance [11].

Data mining is one of the areas of the knowledge data discovery process and is capable of providing innovative avenues for interpreting data. Data mining comprises the extraction of significant and implicit, previously unidentified, and possibly valuable information from data. Data mining offers the ability to detect patterns that are hidden amid a set of data. Data mining is the process of sorting and classifying data, then finding anomalies, patterns, and correlations in large data sets to predict outcomes. Employing a wide variety of techniques, companies can use this information for problem detection, quality control, increase revenue, cut costs, improve customer relationships, and reduce risk, among others [12]. Since modern semiconductor manufacturing processes suffer from a great degree of complexity, and the amount of data is overwhelming, it is still challenging to reach fast yield improvement by discovering manually useful patterns in raw data [11].

Throughout wafer manufacturing, equipment data, process data, and the historic data will be semiautomatically or automatically collected and grouped in a database in order to be able to diagnose faults, to monitor the process, and to effectively manage the production process. Nevertheless, in such advanced manufacturing units such as semiconductor production, numerous aspects and details are interconnected and have an effect on the yield of the produced wafers [13]. Therefore, data mining techniques are a solution for a significant amount of challenges that the semiconductor manufacturing faces, such as yield improvement [5,11], quality control [14], fault detection [15], predictive maintenance [16], virtual metrology [17], scheduling [18], business improvement [19], and market forecasting [20], among others.

Despite the existence of a high number of studies regarding data mining applications in semiconductor manufacturing, a gap was identified in the literature, in which the necessity to compile and analyze in a more comprehensive way through the compilation in a single paper every published study arose, and expressly perform it without restrictions on location or characteristics. With the intention of filling the identified gap in the research, the aim of this paper is to compile all the existing publications on this topic on Scopus and WoS and to classify and compare them. Therefore, one of the goals of this study is to understand the state of the art regarding data mining solution to existing challenges in semiconductor manufacturing. A bibliometric study is presented, in which are analyzed the number of publications over time, the co-occurrence network, the most cited authors, the distribution of keywords by observed frequency, among other bibliometric metrics. This analysis, besides analyzing bibliometric indicators and making a comparison between distinct features, it also has the purpose to frame these indicators in distinct categories and highlighting every case, not only to seek and detect future research pathways, but also to have a better comprehension of data mining applications in semiconductor industry and to endorse it in order to disseminate its use.

This paper is organized as follows. In Section 2, a brief overview of the semiconductor manufacturing process is given. In Section 3, a structured bibliometric analysis is made. In Section 4, a qualitative organization and analysis data mining application studies in semiconductor manufacturing can be found. In Section 5, a brief result analysis and discussion is made. Finally, in Section 6, overall conclusions are given.

2. Bibliometric Analysis

According to the literature, a systematic literature review neutralizes the perceived weaknesses of a narrative review [21]. A systematic literature review usually has distinct stages of preparation, direction-finding and publishing, and diffusion. Every stage might comprise numerous steps of the review process by being part of a method or system that is created to precisely and objectively focus on the overall question the review is bound to answer. In this study, the research design applied in [21,22,23,24] was followed, as seen in Figure 1, by comprising five steps: problem conception; literature search; research evaluation; research analysis; and finally result summarizing.

The objective of this bibliometric analysis is to know the state-of-the-art of data mining application in the semiconductor manufacturing. In a scenario where companies store large amounts of data, data mining approaches are used to extract useful information and knowledge automatically [25]. To achieve that, data mining approaches use a combination of algorithms and concepts from artificial intelligence, statistics, machine learning, and data management [26]. Accordingly, in this bibliometric analysis we look for data mining applications in semiconductors where authors attempt to extract information and knowledge in semiconductor manufacturing from large datasets.

After the topic of data mining data mining applications in semiconductor manufacturing was selected as an object of intensive study in this literature review, an extensive bibliographic research was carried out on the subject and its surroundings. The purpose of this analysis is to identify and evaluate the adopted methodologies of data mining applications in semiconductor manufacturing, by taking into account all the scientific studies found.

The research methodology was carefully developed in order to allow the identification of relevant patterns and areas for the study under analysis. The literature research process comprises such characteristics as the collected qualitative and quantitative information being well defined and delimited, a detailed analysis being made based on the evidence and characteristics recognized in the subject of the study, the analyzed papers are organized by application areas, all contents are analyzed in a qualitative manner, which favors the identification of important subthemes and the successful interpretation of results. We considered papers that address the application of data mining to exploit data stored during semiconductor manufacturing processes. So, in the first step, the usefulness of each article was verified by reading its summary and introduction, so that those who seemed to be out of the review due to imprecision and a lack of details were excluded. Additionally, despite that some of the data mining algorithms and techniques may be applied by semiconductor manufacturing authors, we excluded any papers that do not approach its use for information and knowledge extraction. After defining the aforementioned delimitations, a more detailed analysis was made on the articles that effectively added value in their incorporation in the review article. The purpose of data mining application has been carefully revised. This more detailed analysis includes: a selective reading and choice of material that suits the objectives and proposed theme; an analytical reading of the texts grouping them by application areas; and concludes with the interpretative reading and writing of the literature review body.

After the main elements of the research process have been well established, it becomes essential to adopt some essential assumptions for the accomplishment of this analysis. First, following the guidelines from [27], only indexed and peer-reviewed articles were taken into account, and the indexing databases considered were Scopus and Web of Science (WoS). The keywords utilized were “Data Mining” and “Semiconductor Manufacturing”, which garnered the highest number of results. However, also, all the possible variants, such as “Semiconductor Fabrication”, “Semiconductor Production”, and “Semiconductor Packaging” were utilized in order to cover all the possible published papers through this combination.

Table 1. Results from different combinations of keywords in the database.

Search Stream	Results
	Scopus	WoS
“Data Mining” AND “Semiconductor Manufacturing”	142	87
“Data Mining” AND “Semiconductor Fabrication”	11	9
“Data Mining” AND “Semiconductor Production”	8	5
“Data Mining” AND “Semiconductor Packaging”	2	2

shows the results from different combinations of keywords in the database.

The publications considered for this study were publications in English and the type of articles were journal research articles, journal review articles, conference articles, book chapters, and editorials. A few papers were found in Chinese and Polish, but were excluded from this study. In Figure 2 the flowchart of the paper selection process can be observed. In the end, a final sample of 137 papers was used for the article analysis. This sample comprises almost all papers found with the keywords used.

All the selected studies were classified by year and the result can be seen in Figure 3. Three waves can be seen, the first wave that comprises paper from 2004 to 2007 peaked in 2006 with 10 publications and then the interest waned. The second wave peaked in 2014 and comprises the years 2011 until 2015. Finally, the last wave of interest in this topic can be seen, peaking in 2019, with 12 publications. This wave is still ongoing. However, if divided by decades, one can notice that the decade 2010–2020 comprises 64% of all publications, while the previous decade comprises only 33.5%. This interest reveals the growing scientific interest in this topic. This increase coincides with the overall interest in data mining applications for other industries [28,29].

A particular importance has to be given to the papers that garner the highest interest in the community, which is measured by the number of citations that a study has. Figure 4 shows the most cited studies of data mining applications in semiconductor manufacturing, according to Scopus. It can be observed that the first four articles are much more cited than the remaining ones. The most cited paper is proposed by [30] and deals with maintenance. It addresses a multiple classifier machine learning technique for predictive maintenance in the ion implantation process, and, at the time of the writing of this study, it is only 5 years old. The second most cited article is an overview data preprocessing with two examples, with one in semiconductor manufacturing [31]. This study has more than two decades and it is one of the main reasons why it has 185 citations. The third most cited study deals with quality issues and proposes a framework that combines traditional statistical methods and data mining techniques for fault diagnosis and low yield product for the process of wafer acceptance testing and probing [13]. Finally, the fourth most cited study, with 168 citations, addresses a rule-structuring algorithm based on rough set theory to make predictions for the semiconductor industry [32]. This study is focused on decision support systems and has almost two decades. Still, these four studies, which address data mining applications in different contexts and areas of semiconductor manufacturing and distinct subprocesses, are an example of how vast the applications of data mining techniques in this process are. The interest that these studies attracted is a staple in their respective subcategories of semiconductor manufacturing. Lotka’s Law states that the large number of small paper producers bring together about as much as the small number of large paper producers [33]. The frequency distribution of scientific productivity according to Lotka’s law is shown in Figure 5, Chen-Fu Chien being the most productive author. This can also be observed in Figure 4, in which Chen-Fu Chien is the author of nine of the most cited papers, since Chen-Fu Chien is also a coauthor of the fifth [34] and last [5] most cited papers from this figure.

Keyword Analysis

A bibliometric keyword analysis was performed. This analysis was made with the help of VOSViewer software [35] and biblioshiny, which is a web application for Bibliometrix, and R Package [36]. Both have similar but distinct applications. First, the intention was to identify which were the most employed keywords. Therefore, a keyword analysis with VOSViewer software was performed with the main goal to evaluate the specifics of the discussion on how data mining applications in semiconductor manufacturing.

For the goal of this paper, the Keywords Plus function has been employed with the purpose of harmonizing the keywords that other authors have employed in the Abstract and Keyword section of their respective publications. This analysis shows that 2845 keywords were employed in the selected studies. However, only 51 of these terms appear at least 12 times. The six keywords with the highest occurrences are “data” (which appears 264 times), process (which appears 134 times), system (appearing 117 times), approach (appearing 109 times), and, finally, terms “model” and “semiconductor manufacturing” (both appearing 94 times). The network of co-occurrence links between these keywords is also shown in this paper with the intention of complementing the analysis of keywords co-occurrence. The generated keywords co-occurrence network map can be observed in Figure 6. Three different clusters can be observed.

However, another analysis was made with biblioshiny of the Bibliometrix, from the R Package. With this application it is possible to go more in-depth regarding keyword analysis. Here, only keywords inserted by the authors of their respective papers were considered. The top five keywords that are inserted more often are “data mining”, “semiconductor manufacturing”, “machine learning”, “feature selection”, and “yield enhancement”. However, by making just this simplified analysis not enough can be deduced. In Figure 7 the obtained frequency chart with biblioshiny can be observed with the distribution of the 47 most often found keywords in the selected sample of papers. A total of 349 keywords were found through the simplified technique employed in [37] to represent Zipf’s law. This law stated that certain terms occur much more frequently than others and the distribution is similar to a hyperbole 1/n. As the authors from [37], however, the occurrence of the keywords is stratified in decreasing order of frequency and categorized into three areas of analysis. First, the most important zone represents the basic or trivial information area, which shows the most essential terms on the subject. The second zone comprises the terms considered “interesting information”. This zone can comprise potentially innovative information and fringe themes. Finally, the last area is the noise zone. This area could represent concepts not yet emerging or even simply, noise.

3. Semiconductor Manufacturing Process

The term “semiconductor” refers to a critical component in millions of electronic devices employed in current daily lives in education, research, communications, healthcare, transportation, energy, and other industries. Smartphones, mobile, wearable devices rely on semiconductors for both core operations and advanced functions and are driving global demand for semiconductors and printed circuit boards (PCBs).

The line width of semiconductors has undergone a drastic reduction, passing from the micrometer to the nanometer scale, while, in parallel, the process power and memory have been increased. Integrated circuits, made of a semiconductor material (such as silicon), are an important part of modern electronic devices in both commercial and consumer industries. These circuits must have the ability to act as an electrically controlled on/off switch (transistor) in order to perform basic arithmetic operations in a computer. To achieve this almost instantaneous switching capability, the circuits must be made of a semiconductor material, a substance with electrical resistance that lies between a conductor and an insulator.

The manufacturing process for semiconductor devices requires several steps that take place in highly specialized facilities. Semiconductor production is a considerably complex process with long lead times that are necessary to deliver the capabilities expected from everyday use of our devices. The semiconductor production times vary depending on the complexity; however, on average, it can take three to five years from initial research to final product.

Highly pure silicon is the most important raw material for the production of microelectronic components such as ICs, microprocessors, and memory chips. Figure 8 shows a summarized version of the manufacturing process. The first step in manufacturing a semiconductor device is to obtain semiconductor materials, such as germanium, gallium arsenide, and silicon, of the desired level of impurities [38,39]. Impurity levels of less than one part in a billion are required for most semiconductor manufacturing [40,41]. Due to the microscopic size of semiconductors, even the slightest hint of contamination can compromise their performance. The partly aggressive liquids required in the further manufacturing process of the microchips for metallizing, developing, etching, and cleaning should be safely conveyed, circulated, and processed [42].

The second main step is the crystal growth of monocrystalline silicon and growth of multicrystalline ingots [43]. Then, from these ingots, wafers are cut, and then shaped, polished, and cleaned with the purpose of being ready for further processing or for device manufacturing [44]. To achieve a functional device with predetermined specifications as a final result, it is necessary to carry out a prior design process for each of the manufacturing steps and a mask design, especially, for the masks used in the photolithographic processes that makes semiconductor manufacturing possible. The mask comprises the master copy of the pattern that will be printed on the wafer [45].

The next important step consists of chemical mechanical planarization or chemical mechanical polishing (CMP) is a process in which topographical irregularities can be removed from wafers with a combination of chemical and mechanical (or abrasive) polishing in order to obtain the smoothest surface possible [46,47]. The process is usually used to planarize oxide, polysilicon, or metal layers in order to prepare them for the subsequent lithographic step [48,49]. During ion implantation, high-energy ions are shot onto the substrate to be doped by the doping agent. The distribution of the implanted atoms in the semiconductor can be specifically influenced by the energy, the entry angle, and the use of masks. With multiple implants carried out one after the other, even complex doping profiles can be produced with good accuracy and replicability [50,51].

As seen in Figure 8, one of the most important steps in semiconductor manufacturing is extreme ultraviolet (EUV) lithography a process that allows carving more electrical circuits in semiconductor silicon wafers. In a lithographic system, images are transferred to silicon with light [52,53]. EUV lithography is considered to be essential to semiconductor manufacturing since it is able to produce a shorter wavelength that allows a greater quantity of electrical circuits to enter a chip [54]. Then, an important step is etching, which is utilized in microfabrication to chemically eradicate layers of a material from the surface of a wafer in order to create a pattern of that material on the substrate [55].

The following step is wafer probing, which is the procedure of electrically verifying each die on a wafer. This is accomplished by utilizing an automatic wafer probing system, which is actively searching for functional defects through by employing special test patterns [56,57,58]. The next step, semiconductor packaging and assembly process, involves enclosing ICs and encompasses from die-attach adhesives to liquid and film-shaped encapsulation compounds, sealing, lead forming/trimming, deflash, wirebonding, lead finish to heat-conducting materials, and conductive and non-conductive adhesives for sensors, among others. The encapsulation technology protects the sensitive layers from external influences and maintains their efficiency [59,60]. Finally, the final component is carefully tested in order to verify if it meets the requirements of standard specifications. The testing process is employed to test semiconductors in the context of design verification, specialized production, and quality assurance [61].

4. Data Mining Applications in Semiconductor Manufacturing

Data mining techniques can have a vast array of applications in the semiconductor industry. The obtained articles were classified accordingly to areas of application. Five major areas for data mining applications in semiconductor manufacturing emerged: quality control, maintenance, production, decision support systems, and finally, categorized as a whole, measurement, metrology, and instrumentation. However, other applications also exist, such as for human resources and talent recruitment and retainment [62], patent analysis [63], supply chain and inventory management [64], and stock market analysis [20], proving that data mining techniques can truly be employed for a wide range of applications.

Figure 9 shows the schematic representation of these applications. In some cases, only one article exists, and as such the direct reference is provided. In other cases, the identified five major areas are divided by subsections, in which a more detailed analysis is made. Additionally, this section is also useful for practicing engineers, since they can quickly find the semiconductor process step or data mining model they are looking for. They can also find the study that has been implemented and validated in industrial setting and through corresponding references, access to it.

4.1. Data Mining Applications for Quality Control

Misaligned image processing can cause thousands of auxiliary operations and damaged wafers during a machine’s life during the photolithography process, wafer scrutiny and inspection, or wafer mounting and cutting [65]. Inefficient image processing systems cost semiconductor companies market share and contribute significantly to their overall costs [66]. Data mining techniques are able to provide robust, precise, and fast wafer and chip pattern location for wafer inspection, probing, assembly, cutting, and test equipment to avoid such types of problems. These techniques allow manufacturers to control the quality of wafers and chips with high precision and accuracy, ensuring reliable equipment performance during the semiconductor manufacturing process.

The main purpose of quality prediction tools is to forecast the behavior of the product and then to be able to also forecast the trends of values of its critical parameters, typically accomplished by employ learning functions that have the capacity to stem knowledge from the preceding information. Forecasting quality with the help of data mining techniques normally starts by creating a model based on previous data, for instance labeling samples, and then assess and verify the unidentified samples, or to evaluate, from a given sample, the attributes’ value ranges [67].

Table 2. Data mining applications for quality control in distinct steps of semiconductor manufacturing.

Year	Overall Proposal	Proposed/Used Algorithm	DM Techniques	Real World Dataset	Real World Validation	Location of Dataset or Company	Refs.
2020	A review of data mining applications for quality control of semiconductor manufacturing	Several	Several	No	No	-	[67]
2020	Correctly identifying actual defective patterns in Wafer Bin Maps (WBM) to support the improvement of production yield	Hybrid clustering algorithm that integrates cluster analysis and spatial statistics	Clustering	Yes	Yes	-	[68]
2020	A new approach of measuring similarity of wafer bin maps in order to improve defect diagnosis and fault detection	Mountain clustering algorithm Weighted Modified Hausdorff Distance (WMHD)	Clustering	Yes	Yes	Taiwan	[10]
2020	An Expected Margin–based Pattern Selection model, that is able to select patterns based on an estimated margin for Support Vector Machines (SVMs) classifiers for wafer quality classification in the photolithography process	Expected Margin-based Pattern Selection (EMPS) Support Vector Machines (SVMs)	Classification	Yes	Yes	South Korea	[69]
2019	Fault detection and diagnosis model directly taken from the variable-length status variables identification (SVID) in the etch process	Convolutional neural networks (CNNs)	Classification	Yes	Yes	South Korea	[70]
2019	Clustering-based defect pattern detection and classification framework for WBMs	Density-based spatial clustering of applications with noise (DBSCAN)	Clustering	Yes	No	-	[71]
2019	An yield prediction model based on the selected critical process steps by taking into account difficulties such as imbalanced data, random sampling, and missing values	Expectation maximization (EM), MeanDiff technique, Synthetic minority over-sampling technique (SMOTE), decision tree, logistic regression, k-nearest neighbors (k-NN), and SVM	Classification Regression	Yes	No	-	[9]
2018	A framework based on Bayesian inference and Gibbs sampling to investigate the intricate semiconductor manufacturing data for fault detection	Bayesian inference, Gibbs sampling, high dimensional linear regression, multivariate adaptive regression spline (MARS), Cohen’s kappa statistics	Classification	Yes	No	-	[5]
2018	Process errors detection and practical process improvement	Decision tree-based classification C4.5 in KNIME	Association rules	Yes	Yes	France	[19]
2018	A robust incremental on-line feature extraction method by ensuring the accuracy of data analysis and by meeting real-time demands of semiconductor manufacturing process for product quality supervision	PCA (Principal Component Analysis)RIPCA (Robust Incremental Principal Component Analysis) CCIPCA (Covariance-Free Incremental PCA)	(+)Feature selection/Dimensionality reduction	Yes	No	-	[72]
2018	Data mining applications semiconductor manufacturing process quality control	Fisher criterion algorithm, Support Vector Machines (SVMs) and Random Forest	Classification	Yes	No	Northern Ireland	[73]
2018	A mutually-exclusive-and-collectively-exhaustive feature selection framework applied to two cases of datasets, one being from a real manufacturing process	Mutually-exclusive-and-collectively-exhaustive (MECE) Two-phase clustering selection (TPS), stepwise selection (SS) Chi-Square Automatic Interaction Detector (CHAID)	(+)Feature selection/Dimensionality reduction	Yes	No	-	[74]
2017	Yield analysis operation performed by engineers with the aim of identifying the causes of failure from wafer failure map patterns and manufacturing historic records. An integrated automated monitoring system with deep learning and data mining techniques is proposed.	Convolutional Neural Networks (CNNs), Support Vector Machine (SVM), Clustering and pattern mining methods of K-Means++ and FPGrowth	Classification Clustering	Yes	No	-	[11]
2017	A data-driven approach for analyzing semiconductor manufacturing big data for low yield diagnosis purposes for detecting process root causes for yield improvement	Random Forest	Regression	Yes	Yes	Taiwan	[75]
2017	Comparison between Angle Based Outlier Detection (ABOD), Local Outlier Factor (LOF), onlinePCA (online Principal Component Analysis) and osPCA (os Principal Component Analysis) for semiconductor Manufacturing Etching process	Angle Based Outlier Detection (ABOD), Local Outlier Factor (LOF), onlinePCA, osPCA	(+) Outlier detection	Yes	No	-	[76]
2015	A statistical comparison of fault detection models for six datasets which were obtained by simulating of a plasma etching machine for a semiconductor manufacturing etching process	Support vector machine recursive feature elimination (SVM-RFE), principal component analysis (PCA), (k-nearest neighbors (kNN), SVMs, neural network (NN), logistic regression, partial least-squares discriminant analysis (PLS-DA), decision tree, squared prediction error, multi-way principal component analysis (MPCA)	Classification (+)Feature selection	No	No	-	[77]
2016	A simulator that carefully mimics data from a real etching process in a wafer production for the identification and prediction of unspecified situations by adopting data mining techniques to derive predictive patterns in order to detect flows and failures	Decision Tree, Naïve Bayes, Support Vector Machines with k-Means and hierarchical clustering	Regression Classification	No	No	-	[78]
2016	A wafer fault detection and essential step identification for semiconductor manufacturing by employing principal component analysis (PCA), AdaBoost and decision trees	Adaptive Boosting algorithm, decision trees, principal component analysis (PCA), SVMs	Classification	Yes	No	-	[79]
2016	Predictive analytics methods and its application in improving semiconductor manufacturing processes by considering several situations in semiconductor fabrication	Artificial neural networks (ANN), Clustering Method- K- Nearest Neighbor, robust regression	Classification	Yes	No	-	[80]
2015	A framework based on a linear model in order to obtain the weight tensor in a hierarchical manner for wafer quality prediction in semiconductor manufacturing	Hierarchical Modeling with Tensor inputs (H-MOTE algorithm), ridge regression, potential support vector machine (PSVM), tensor least squares (TLS)	Regression	Yes	No	-	[81]
2015	A data driven framework for degraded pogo pin detection in semiconductor manufacturing integrated circuit product testing process	Linear regression and classification algorithms (unspecified)	Regression Classification	Yes	No	USA	[82]
2016	A multi-feature sparse stacking-based approach for detecting defects and classification in produced semiconductor units	A proposed multi-feature sparse-based classification model Other models for comparison	Classification	Yes	No	Intel (USA)	[83]
2015	A combination of distinct data sources with the intention of identifying yield loss causes. The test is on a production step, comprising an implantation manufacturing step and its quality control step, a test done during the wafer sorting/probing (or wafer test).	K-means algorithm, “a priori” association rules mining algorithm, decision trees	Clustering Association rules	Yes	Yes	France	[84]
2014	A design-of-experiment (DOE) data mining for yield-loss diagnosis for semiconductor manufacturing (lithography, etching, among others) by detecting high-order interactions and show how the interconnected factors respond to a wide range of values	Regression analysis, Kruskal–Wallis test, Dunn’s test, Holm–Bonferroni method, closed test procedure	Regression	Yes	Yes	Taiwan	[85]
2014	A yield analysis method employing basic yield and in-line defect information to statistically determine significant root-causes of yield loss in semiconductor manufacturing	Proposed yield accounting system, other unspecified	Classification	Yes	Yes	USA	[86]
2014	A morphology-based support vector machine for similarity search of binary wafer bin maps defect patterns during the probing test for yield enhancement	Support Vector Machines (SVM), morphology-based SVM (MSVM), Receiver Operating Characteristic (ROC), mountain method clustering	Classification	Yes	Yes	Taiwan	[87]
2014	Sequence mining and decision tree induction, to discover frequently occurred patterns of the low performance wafer lots in the semiconductor manufacturing industries	Decision Trees, Sequence Mining	Classification Association rules	No	No	-	[88]
2014	A united outlier detection framework that uses data complexity reduction by employing entropy and abrupt change detection using cumulative sum (CUSUM) method. Over an 8-month use period, the developed method was applied to reactive ion etching (RIE) and photolithography tools and recipes.	Algorithm I—Data Complexity Reduction Using Entropy Algorithm II—Abrupt Change Detection Using CUSUM	(+)Outlier detection	Yes	Yes	IBM (USA)	[89]
2014	A framework for root cause detection of sub-batch processing system in wafer testing and probing process	Random forest (RF), Sub-batch processing model (SBPM)	Regression	Yes	Yes	Taiwan	[90]
2013	An online detection and classification system of wafer bin map defect patterns during circuit probing tests	ART1 Neural Network Adaptive Resonance Theory algorithm	Classification	Yes	Yes	Taiwan	[91]
2013	Employment of k-means clustering algorithm by enhancing Support Vector Machines (SVM). Experiments with the real data of a semiconductor test process is given	K-means, Support Vector Machines (SVM), Synthetic Minority Over-sampling Technique (SMOTE)	Clustering	Yes	No	-	[92]
2013	A framework for semiconductor fault detection and classification (FDC) to monitor and analyze wafer fabrication profile data for the CVD Ti/TiN vapor deposition process	Principal component analysis (PCA), Multi-way PCA (MPCA), self-organizing map (SOM) neural network	Classification	Yes	Yes	Taiwan	[93]
2012	An optimization framework for hierarchical multi-task learning, which partitions all the input features into two sets based on their characteristics applied in the process of depositing dielectric materials as capping film on wafers	HEAR algorithm (MTL with Hierarchical task Relatedness) based on block coordinate descent	Classification	Yes	No	-	[14]
2012	A main branch decision tree (MBDT) algorithm that diagnoses the root causes and provides quick responses to irregular equipment operation in the wafer acceptance testing and probing processes with imbalanced classes	Main branch decision tree (MBDT) algorithm	Classification	Yes	Yes	-	[94]
2012	A two-phase morphology-based similarity search for wafer bin maps in semiconductor manufacturing for wafer acceptance testing	Support Vector Machines (SVM)	Classification	Yes	No	-	[95]
2011	A technique based on the data mining technology to automatically generate an accurate model to predict faults during the wafer fabrication process of the semiconductor industries	Principal component analysis (PCA), cluster technique MeanDiff, decision tree, naïve Bayes, logistic regression, and k-nearest neighbor	Regression Classification	Yes	No	-	[96]
2019	An altered AdaBoost tree-based method for defective products identification in wafer testing process	AdaBoost Tree-based method Synthetic Minority Oversampling Technique (SMOTE) + Edited Nearest Neighbor (ENN)—SMOTE-ENN algorithm	Classification	Yes	No	-	[97]
2006	Wavelet-based data reduction techniques for fault detection in rapid thermal chemical vapor deposition processes (RTCVD)	Discrete wavelet transforms, classification and regression tree (CART)	Classification Regression	Yes	No	-	[15]
1999	Effectiveness of association rules and decision trees data mining techniques in determining the causes of failures of a wafer manufacturing process	Association rules and decision trees	Association rules Classification	Yes	No	-	[98]
2008	A spatial defect diagnosis system at the probing test which estimates number of clusters in advance and separates both convex and non-convex defect clusters at the same time	Decision trees, a method merging entropy fuzzy c means (EFCM) with Kernel based spectral clustering	Classification	Yes	Yes	Taiwan	[99,100]
2007	A framework that combines traditional statistical methods and data mining techniques for fault diagnosis and low yield product for wafer acceptance testing and probing	Kruskal–Wallis test, K-means clustering, and the variance reduction splitting criterion, decision trees	Clustering Classification	Yes	Yes	Taiwan	[13]
2007	A hybrid data mining method that integrates spatial statistics and adaptive resonance theory neural networks to extract patterns from WBMs	Adaptive resonance theory (ART), Decision trees, Classification and regression tree (CART)	Classification	Yes	Yes	Taiwan	[34]
2007	A Bayesian networks to extract knowledge from data ant the purpose is to implement a data mining task for computer integrated manufacturing (CIM). The end goal is to encounter the cause factors in various parameters which have an effect during the wafer cleaning process	Bayesian networks, directed acyclic graph, decision trees	Classification	Yes	Yes	-	[101]
2007	Data mining technique by utilizing Gradient Boosting Trees for predicting class test yield performance at high volume semiconductor manufacturing after assembly and final testing	Gradient boosting trees (GBT) ensemble algorithm	Regression	Yes	Yes	Intel (Malaysia)	[102,103]
2006	An on-line diagnosis system that relies on denoising and clustering methods for identifying spatial defect patterns in semiconductor manufacturing processes	Integrated clustering scheme combining fuzzy C means (FCM) with hierarchical linkage, decision trees	Clustering	Yes	Yes	Taiwan	[104]
2006	A data mining technique to predict and classify the product yields in semiconductor manufacturing processes in wafer acceptance testing and probing	Genetic programming, Decision trees	Classification	Yes	Yes	Taiwan	[105]
2000	A combination of self-organizing neural networks and rule induction employed in the identification of poor yield factors from collected wafer probing manufacturing data	Self-organizing neural networks and rule induction	Classification Association Rules	Yes	Yes	USA	[106]

shows the categorized papers by data mining applications for quality control in distinct steps of semiconductor manufacturing. These steps are identified, when possible, and can be found in the summary proposal. The table is subdivided into eight major columns and in a few can be observed the year of publication, reference, and the overall summarized description of the study. One of the remaining columns describes the proposed and/or used data mining algorithm, which can be helpful by quickly identifying a specific algorithm. The next column shows which DM technique is used. The remaining columns show if the sample data is collected from a real production site or if it was simulated, and if it is real, it is identified, when possible, by company and country of origin. Additionally, if experimental validation studies were performed on site, it is also highlighted.

This topic is the most popular one, with 47 publications. By observing Table 2, it can be seen that several applications are made in distinct subprocesses such as wafer probing and testing process, etching process, and photolithography, among others. A high and varied number of algorithms are employed. The majority of articles address challenges of correctly identifying defective patterns in order to improve production yield [68]. Yield is a quantitative measure of the quality of a semiconductor process. It is measured as the number of functioning dies or chips on a wafer and can also be seen as the fraction of dies on the yielding wafers that are not rejected during the production process [107]. However, other applications in quality control can also be found, such as a study addressing a design-of-experiment (DOE) data mining for yield-loss diagnosis for semiconductor manufacturing by detecting high-order interactions, for subprocesses such as lithography and etching, among others [85]. These data mining technique are also used with statistical process control. Cumulative sum control charts, known as CUSUM, are a special type of statistical process control tool that is used in [89] as part of and unified outlier detection framework, which takes advantages of data complexity reduction by employing entropy and sudden change detection through the use of CUSUM charts.

4.2. Data Mining Applications for Maintenance

Only a few articles were published addressing maintenance management and prediction, but are important nonetheless. Only five papers were classified and can be observed in

Table 3. Data mining applications for maintenance prediction and management in semiconductor manufacturing.

Year	Study Proposal	Proposed/Used Algorithm	DM Techniques	Real World Dataset	Real World Validation	Location of Dataset or Company	Ref.
2017	Hidden Markov model-based predictive maintenance for semiconductor wafer production equipment, recorded over one year	Preliminary fitting of a hidden Markov model (HMM) Genetic, genetic algorithm		Yes	No	-	[108]
2016	Predictive Maintenance with time-series data based on Machine Learning tools in Ion implantation	Supervised Aggregative Feature Extraction (SAFE)		Yes	No	-	[16]
2015	A multiple classifier machine learning technique used for predictive maintenance in Ion implantation process	Support Vector Machines k-Nearest Neighbors	Classification Clustering	Yes	No	-	[30]
2012	Data mining technique that is able to deliver early warning by identifying tool excursion in real time for advanced equipment control in order to diminish abnormal yield loss	Decision trees, Chi-Squared Automatic Interaction Detector, Rough set theory	Classification	Yes	Yes	Taiwan	[109]
2008	Spatial pattern recognition to improve the identification and resolution of rogue and possibly malfunctioning tools in semiconductor manufacturing	Spatial pattern recognition	(+)Feature selection	Yes	Yes	AMD (USA)	[110]

. This table is organized as Table 2. As it can be noticed, these studies are sparse and the majority were published in the last 8 years. However, the most cited article is a study in this area of application. In this study a multiple classifier machine learning methodology for predictive maintenance in the ion implantation subprocess is proposed [30] and a similar study is proposed in [16]. In another study, hidden Markov model-based predictive maintenance for semiconductor wafer production equipment and documented over one year was proposed in [108]. A data mining technique that is able to deliver early warning by identifying tool excursion in real time for advanced equipment control in order to diminish atypical yield loss is proposed in [109] and was validated by practical applications in the field. Finally, the last study addresses spatial pattern recognition in order to improve the resolution and identification of defective and malfunctioning tools in semiconductor manufacturing developed and implemented at Advanced Micro Devices, Inc. (AMD) [110].

4.3. Data Mining Applications for Metrology, Measurement, and Instrumentation

The high necessity for always striving to make progress regarding the yield of current semiconductor production processes and decrease the time-to-market for more advanced, innovative, and gradually elaborate designs and processes demands for process tools and wafers to be examined and verified with up-to-date measurement systems and equipment. Several papers, namely 19, are categorized in this topic, as depicted in

Table 4. Measurement, metrology, and instrumentation data mining applications.

Year	Study Proposal	Proposed/Used Algorithm	DM Techniques	Real World Dataset	Real World Validation	Location of Dataset or Company	Ref.
2019	Automatic method for extraction of signatures from the raw data generated by non-rotating equipment	Virtual metrology Genetic Algorithms	(+)Feature selection	Yes	No	-	[120]
2019	A Deep Learning method for Virtual Metrology that employs semi-supervised feature extraction reliant on Convolutional Autoencoders for a 2-dimensional Optical Emission Spectrometry data	Convolutional Neural Networks Deep Learning Virtual metrology	(+)Feature selection	Yes	No	-	[115]
2019	A feature extraction technique for virtual metrology with multisensor data in semiconductor manufacturing that relies on deep autoencoder which also offers a clipping fusion regularization on the signals reconstructed by deep autoencoder in the case of an etching process for wafer fabrication	Principal component analysis (PCA) Virtual metrology, unsupervised deep autoencoder (AE)	(+)Feature selection	Yes	No	-	[17]
2016	A Euclidean distance- and standard deviation-based characteristic selection and over-sampling used in a fault detection prediction model and applied to measure performance	Principal component analysis (PCA), SVM (Support Vector Machine), C5.0 (Decision Tree), KNN (K-nearest neighbor), Artificial neural network (ANN)	(+)Feature selection Classification	Yes	No	-	[121]
2017	OpenMV—a low-power smart camera with wireless sensor networks and machine vision applications, it is scripted in Python 3 and comes with an extensive machine vision library	Support vector machine-like (SVM-like) algorithm	Classification	No	No	-	[122]
2014	A precise semiconductor photolithography process control method using virtual metrology using significant correlations between focus measurement data found by data mining and tool data	Virtual metrology Correlation coefficient mining algorithm	(+)Feature selection	Yes	Yes	-	[111]
2014	A Feature Selection wrapper method aiming to find the most important process parameters for smart virtual metrology for High Density Plasma (HDP) Chemical Vapor Deposition	Virtual metrology, Evolutionary Recursive Backward Elimination (ERBE) algorithm, Genetic Algorithms, Support Vector Regression (SVR)	Regression	Yes	Yes	-	[116]
2014	A framework in which the structural information from etching is interpreted as a set of constraints on the cluster membership, an auxiliary probability distribution is then introduced, and the design of an iterative algorithm is prosed for assigning each time series to a certain cluster on every dimension	K-Means algorithm, C-Struts framework, complex-valued linear dynamical systems (CLDS)	Clustering	Yes	No	-	[123]
2013	Data Mining utilizing machine learning techniques for modeling unknown functional interrelations in the high-density plasma chemical vapor deposition process. It predicts the layer thickness through Support Vector Regression	Support Vector Machine (SVM), Support Vector Regression (SVR)	Classification	Yes	No	-	[124]
2013	Data Mining using Machine learning methods to model to model unknown functional interrelations and to predict the thickness of dielectric layers deposited onto a metallization layer of the manufactured wafers.	Decision Trees (DT) Neural Networks (NN) Support Vector Regression (SVR)	Classification Regression	Yes	No	-	[118]
2011	A qualitative clustering method is given, and a comparison is made between a Virtual Metrology (VM) system running on groups of data with the same targets and one obtained by considering the three chambers of the Chemical Vapor Deposition equipment as separated machines	Back Propagation Neural Networks (BPNN) Partial Least Square (PLS) Regression	Clustering Classification	Yes	No	-	[125]
2011	A real-time data mining model by using a Segmentation, Detection, and Cluster-Extraction algorithm that is able to accurately and automatically extract defect clusters from raw wafer probe test production data	Segmentation, Detection, and Cluster-Extraction (SDC) algorithm	Clustering	Yes	Yes	Malaysia	[117]
2011	A multivariate feature selection able of handling mixed and complex typed data sets as an initial step in yield analysis to reduce the number of variables	Ensemble-Based Feature Selection algorithm, gradient boosted tree (GBT)	Regression	Yes	No	-	[126]
2011	Development of virtual metrology (VM) prediction models using several data mining technique and a VM embedded R2R control system by employing exponentially weighted moving average (EWMA) based on data from a photolithography production equipment	Decision trees, GA with linear regression, GA with support vector regression (SVR), Principal component analysis (PCA), and kernel PCA, multi-layer perceptron (MLP), k-nearest neighbor regression (k-NN)	Regression	Yes	Yes	South Korea	[127]
2011	A data mining method for automatically identifying and exploring correlations between inline measurements and final test outcomes in analog/RF devices and incorporate domain expert feedback into the algorithm for identifying and removing spurious autocorrelations	Multi-objective genetic algorithm (NSGA-II), Genetic algorithms (GA), Multivariate Adaptive Regression Splines (MARS)	Regression	Yes	Yes	IBM (USA)	[119]
2009	A virtual metrology (VM) system for an etching process in semiconductor manufacturing based on various data mining techniques	Genetic algorithm with support vector regression (GASVR), Principal component analysis (PCA), and kernel PCA, Stepwise linear regression	Regression	Yes	Yes	South Korea	[128]
2006	A 2nd Generation Data Mining system in cooperation with Advanced Process Control (APC) system and that aim to stabilize machine fluctuation in Photolithography Process	Regression tree analysis, proposed 2nd Generation Data Mining algorithm	Regression	Yes	Yes	Fujitsu (Japan)	[129]
2006	A pre-processing procedure used for numerous sets of complex functional data for reducing data size for the support of appropriate decision analysis. This vertical-energy-thresholding (VET) procedure balances the reconstruction error with data-reduction efficiency	Vertical-energy-thresholding (VET), wavelet-based procedure	(+)Dimensionality reduction	Yes	Yes	Nortel (USA)	[130]
2005	An automatic classification of the electrical wafer test maps in order for identifying the classes of failure present in the production lots, especially due to a lithographic process	Commonality analysis (CA), Kohonen’s self-organizing feature maps algorithm	Classification	Yes	Yes	STMicroelectronics(Italy)	[131]

. This table is organized as Table 2. The topics addressed in this section range from models comprising a precise semiconductor photolithography process control method through virtual metrology by employing significant correlations between focus measurement data encountered by data mining and tool data [111].

In fact, virtual metrology is a recurring topic, and is defined as a set of methods that allow predicting the properties of a wafer through sensor data and machine parameters in the manufacturing equipment, thus avoiding the highly expensive physical measurement of the wafer properties [112,113,114]. Since machine data is typically sampled much more often when compared to metrology data, and since machine data becomes immediately available when compared to the delays that frequently occur with metrology tools, an accurate virtual metrology is capable of meaningfully developing the process control and monitoring performance through a constantly supply of real-time forecasted metrology data. A few feature extraction methods for virtual metrology with multisensor data are proposed in [17,115,116].

However, other measurement and instrumentation were also proposed and classified. For instance, in [117] a real-time data mining solution with the segmentation, detection, and cluster-extraction (SDC) algorithm that can automatically and accurately extract defect clusters from raw wafer probe test production data is proposed. Additionally, a data mining that employs machine learning methods with the purpose of modeling unknown functional interrelations and to predict the thickness of dielectric layers deposited onto a metallization layer of the manufactured wafers is proposed in [118]. Finally, at IBM, a data mining technique with the purpose of automatically identifying and exploring correlations between inline measurements and final test outcomes in analog and/or radio frequency (RF) devices and by integrating domain expert feedback into the algorithm in order to identify and remove bogus autocorrelations [119]. Practical application and validation of this technique is made.

4.4. Decision Support Systems

Another trend in semiconductor manufacturing is the use of decision support systems (DSS). A DSS is a system designed to support in solving unstructured and semistructured managerial problems, throughout all the decision process’ stages [132]. The DSS use in this area is not novel. Earliest publications in this area date to the 1990s (e.g., [133,134]). DSSs are used to support decision-making in activities like production scheduling, simulation, prediction, material selection, fault detection, quality, etc. DSSs may, sometimes, have a knowledge base, which requires artificial intelligence to provide knowledge to support the decision process. However, the earliest uses of DSS required knowledge modeling by knowledge engineers from documented and expert knowledge. Knowledge extraction from unprocessed data allowed one to discover hidden knowledge in large amounts of data. The use of data mining techniques to uncover knowledge to be modeled in DSS is a trend also present in semiconductor literature. Researchers apply data mining techniques to find patterns and hidden relations that may help in semiconductor decision making. Usually, the goal is to determine links between control parameters and product quality, essentially in the form of decision rules [135].

In

Table 5. Data mining applications for decision support systems.

Year	Study Proposal	Proposed/Used Algorithm	DM Techniques	Real World Dataset	Real World Validation	Location of Dataset or Company	Ref.
2019	The results for yield improvement of our silicon carbide technology using advanced data analytics by outlining how the data was collected, preprocessed and managed in order to turn it much more appropriate for further analysis	Unspecified	(+)Generic	Yes	Yes	Northrop Grumman (USA)	[149]
2018	A new balanced production method for holistic optimization of operation strategies applied to semiconductor manufacturing	DBSCAN clustering algorithm Genetic optimization algorithm	Clustering	Yes	Yes	-	[150]
2015	Development an analytic framework of design for semiconductor manufacturing and validated through a case study in semiconductor manufacturing concerning the layout design of chip size	Model tree (M5), Regression tree (CART) Neural Network (BPNN)	Regression Classification	Yes	Yes	-	[151]
2013	A framework in which the packaging yield is classified using the parametric test data of the previous step of the packaging test in the post-fabrication process for semiconductor manufacturing	Random forests algorithm, support vector machine (SVM)	Classification	Yes	Yes	SK Hynix Semiconductor (South Korea)	[152]
2012	A procedure for the optimization processes named: values-Patient Rule Induction Method (m-PRIM) by addressing the missing-values systematically	Missing Values Patient Rule Induction Method (PRIM)	Association rules	Yes	No	South Korea	[153]
2001	An integrated relational database method for modeling and collecting semiconductor manufacturing data from multiple database systems and transforming it into useful reports	Integrated Relational Manufacturing Database		Yes	Yes	Motorola (USA)	[154]
2012	Knowledge discovery in databases model that relies on decision correlation rules and contingency vectors to enhance semiconductors manufacturing yield	Association and correlation rules, LHS-CHI2 algorithm	Association rules	Yes	Yes	STMicroelectronics, ATMEL	[135]
2011	Rare class prediction for fault case detection in the wafer fabrication process of semiconductor industries	Decision tree induction, naïve Bayes, logistic regression, k-nearest neighbors	Association rules Classification Clustering	Yes	No	SECOM	[136]
2011	Application of rough set theory, support vector machines and decision trees for improving the quality of decisions of class prediction and rule generation encompassed in human resource management.	Rough sets theory, support vector machines, decision trees	Classification	Yes	Yes	UCI data bank	[147]
2011	Development of a rare case prediction for fault case detection in the wafer fabrication process	Decision tree induction, naïve Bayes, logistic regression, k-nearest neighbors	Association rules Classification Clustering	Yes	No	SECOM	[137]
2010	Propose a system do improve yield, power consumption and speed characteristics using regression rule learning to analyze data collected during wafer production	Regression rule learning, association rules	Association rules	Yes	No	-	[138]
2008	A system to evaluate measurements from a semiconductor production process using feature selection to identify rules	Neural networks, feature selection, simplified fuzzy ARTMAP	Classification	Yes	No	-	[139]
2007	Proposes ensemble classifiers to support decision-making to enhance yield in semiconductor production	Ensemble classification	Regression	Yes	No	.	[140]
2006	Integration of Data Mining techniques in a MES for semiconductor manufacturing	Decision tree	Classification	Yes	No	-	[148]
2006	Combines forward regression and regression tree methods to discover yield loss causes during the yield ramp-up stage	Decision trees, multiple linear regression	Regression	No	No	-	[141]
2005	Uses data mining techniques to design intelligent CIM applied to improve product yield of semiconductor packaging factories.	Decision tree	Classification	No	No	-	[146]
2005	Proposes a model based on decision trees to recognize and classify failure pattern using a fail bit map	Decision tree	Classification	No	No	-	[142]
2004	Proposes a fault detection scheme using a hierarchical fuzzy ruled based classifier to identify defects in wafers	Hierarchical fuzzy rule-based classifier	Classification	Yes	Yes	-	[143]
2003	Proposes a conceptual e-Commerce decision support system that integrates intelligent agents and data mining to help in the sampling process of semiconductor quality	None	(+)Generic	No	No	-	[144]
2001	Proposes the use of neural networks to design in-line measurement sampling methods to monitor and control semiconductor manufacturing	Neural networks	Classification	Yes	No	-	[145]
2001	Proposes a rule-structuring algorithm based on rough set theory to make predictions for semiconductor industry	Rough set theory	Association rules	No	No	-	[32]

the literature where data mining is used to support the decision-making process in semiconductors’ manufacturing is presented. Analyzing this table, one can see that most contributions address yield management and failure detection issues (see [135,136,137,138,139,140,141,142,143,144,145]). The authors from [146] aim at the same problem, but focus on the development of a computer integrated manufacturing (CIM) system to improve product yield. Other articles provide isolated contributions. In [147], the authors propose the application of data mining techniques to support decision-making in HR management of high-tech companies. In [148], the authors suggest the integration of data mining in semiconductor manufacturing execution systems (MES). Last, in [32] provides a multi-purpose data mining application for predictions in semiconductor manufacturing.

4.5. Data Mining Applications for Production and Production Scheduling

Traditional methods for production planning often require complex calculations and do not always allow a prompt reaction to changes or short-term adjustments that may arise. Given the size of the semiconductor production lines in a factory, sensors within production equipment are capable of delivering enormous amounts of data. This data can be, in turn, used not only for machine control, but also for production analysis purposes, especially real-time production planning. This has the potential to bring great advantages, especially in those industrial units in which the production is affected by frequent dynamic changes in the orders to be processed or technical specifications. Additionally, machine learning processes are able to recognize patterns and automatically learn and operationalize practical forecast models from a wide variety of data sources and large amounts of data. Therefore, in the context of semiconductor manufacturing with its complex and numerous subprocesses, numerous data mining applications are proposed for the production and production planning environment.

Table 6. Data mining applications for production in semiconductor manufacturing.

Year	Study Proposal	Proposed/Used Algorithm	DM Techniques	Real World Dataset	Real World Validation	Location of Dataset or Company	Refs.
2004	A decision tree algorithm and classification model are proposed. Intelligent computer integrated manufacturing (CIM) system is applied to semiconductor packaging factories. The manufacturing cycle time, the product yield, and the frequency of holding lot were improved	Decision trees	Classification	Yes	Yes	-	[167]
2020	A new approach that is able to integrate data mining that intends to forecast arrival rates and determine the allocation of interchangeable tool sets in order to decrease the work in process (WIP) bubbles for cycle time reduction	Back-propagation neural network (BPNN)	Classification	Yes	Yes	Taiwan	[155]
2019	A data-driven scheduling knowledge life-cycle management for an intelligent shop floor and validated through a simulated model of the semiconductor production line	Extreme learning machine (ELM), Online sequential extreme learning machine (OS-ELM)	Classification	No	No	-	[162]
2015	A data mining based dynamic scheduling strategy selection model which is able to respond to altering system status in semiconductor manufacturing processes	genetic algorithm K-nearest neighbor algorithm	Clustering	Yes	Yes	-	[18]
2015	A variation reduction of Turn Around Time (TAT) in a semiconductor manufacturing through a data mining-based technique for identifying the root cause of TAT variation	Partial Least Squares Regression (PLSR)	Regression	No	No	-	[168]
2014	A data mining framework that is capable of integrating fault detection and classification and manufacturing execution system data for improving the overall usage effectiveness (OUE) for cost reduction in a Chemical Mechanical Planarization (CMP) process	CHAID (Chi-Squared Automatic Interaction Detection) Decision Trees	Classification	Yes	Yes	Taiwan	[169]
2014	A dynamic scheduling model which optimizes production features subset, and creates an SVM-based dynamic scheduling strategy classification model for semiconductor manufacturing	Particle swarm optimization algorithm (BPSO), support vector machine (SVM)	Classification	Yes	Yes	China	[164]
2013	A noted cycle time forecasting model is developed by employing knowledge discovery in databases by following cross industry standards for data mining	Decision trees, Neural networks	Classification	Yes	No	-	[64]
2013	A Data-based scheduling framework and adaptive dispatching rule for semiconductor manufacturing	Backward propagation neuro-network (BPNN), adaptive dispatching rule (ADR)	Classification	Yes	No	-	[165]
2011	A cycle-time key factor identification and prediction in semiconductor manufacturing by employing data mining and machine learning	Selective naive Bayesian classifier (SNBC) Conditional mutual information maximization (CMIM)	Classification	No	No	-	[170]
2012	A shop floor control system in semiconductor production by self-organizing map-based smart multi-controller showing an improved system performance than fixed decision scheduling rules	Self-organizing map (SOM) neural network	Classification	No	No	-	[166]
2010	Gaussian Processes used for decentralized scheduling with dispatching rule selection in production scheduling for semiconductor manufacturing	Gaussian processes, neural networks	Classification	No	No	-	[171]
2010	A machine learning algorithm capable of implementing an adaptive sequential (A-S) process and accuracy guard band model for improved recipe generation process development in the assembly semiconductor manufacturing processes	Polynomial-based RSM Response Surface Methodology (RSM), Adaptive-sequential (A-S) algorithm	Regression	Yes	Yes	Intel (Malaysia)	[172]
2009	A data-mining approach for estimating the interval cycle time of each job in a semiconductor manufacturing system	Look-ahead self-organization map fuzzy-back-propagation network (SOM-FBPN)	Classification	No	No	-	[156,173]
2009	A data mining methodology which identifies key factors of the cycle time in a semiconductor manufacturing plant which intends to predict its value	Naïve Bayesian classifier (NBC), CRISP-DM (Cross-Industry Standard Process for Data Mining)	Classification	No	No	-	[157]
2004	A hierarchical clustering method that is able to discriminate groups according to the similarity of the objects and used to schedule semiconductor manufacturing processes	Agglomerative hierarchical cluster algorithm	Clustering	No	No	-	[163]

depicts the articles addressing data mining applications for production in semiconductor manufacturing. A total of 16 papers were found in this category. This table is structured as Table 2. It can be noticed that from 2009 until 2015 is when the bulk of these studies were published, then a four-year hiatus was observed. From 2019 can be noticed some interest in the topic.

Many of the studies concerning production planning are focused on reducing cycle time. In [155], a new approach that is capable of integrating data mining that intends to forecast arrival rates and determining the allocation of interchangeable tool sets in order to reduce the work in process (WIP) bubbles for cycle time reduction is proposed. While in another study [64], a cycle time forecasting model is developed by employing knowledge discovery in databases by following cross industry standards for data mining. A data-mining approach for estimating the interval cycle time of each job in a semiconductor manufacturing system is proposed in [156] and a data mining methodology, which identifies key factors of the cycle time in a semiconductor manufacturing plant, which intends to predict its value is addressed in [157].

Scheduling is another concern in semiconductor manufacturing due to its vast number of steps and jobs [158,159,160], confirmed by the majority of the identified studies in Table 6. Efficient order scheduling structures are required for balancing the production load and capacity throughout all the production stages [161]. A data mining dynamic scheduling strategy selection model that is able to respond to a constantly altering system status for a semiconductor manufacturing system is proposed in [18]. In [162] a data-driven scheduling knowledge life-cycle management for an intelligent shop floor is proposed and validated through a simulation model of the semiconductor production line. As early as in 2004 scheduling challenges were a concern, evidenced by a study proposing an hierarchical clustering method in [163] that is able to discriminate groups according to the similarity of the objects and used to schedule semiconductor manufacturing processes. In [164] a dynamic scheduling model, which is able to optimize the production features subset is proposed, and this model is capable of creating a SVM-based dynamic scheduling strategy classification model for semiconductor manufacturing. A data-based scheduling framework and adaptive dispatching rule for semiconductor manufacturing is addressed in [165] by employing backward propagation neuronetworks (BPNNs). Finally, a shop floor control system in semiconductor production by self-organizing map-based smart multicontroller is given in [166]. This study, as all the scheduling studies, showed a better system performance than the typical fixed decision scheduling rules.

5. Discussion

After analyzing all the studies collected in the sample, a few trends begin to be noticed. First, that studies regarding data mining applications in subprocesses such as ICs and mask design are very scarce. The same occurs with studies addressing wafer cutting, cleaning drying, and polishing, while edge rounding and lapping subprocess has no dedicated study. This is better illustrated by Figure 10 in which a representation of several studies depicting data mining applications in several subprocesses of semiconductor manufacturing can be seen. It is noticeable that the majority of studies are concentrated in 5–6 major steps. A few studies do not specify in which subprocess data mining techniques are applied, and these are not represented in Figure 10.

Another trend visible in the analyzed literature is the diverse use of data mining techniques. The application of data mining in semiconductor manufacturing has a different focus depending on the subject areas concerning the manufacturing processes. However, most articles address mainly the issues of quality control, maintenance, and production. Predictive techniques, using algorithms as regression or decision trees, are often used in semiconductor literature to estimate wafer quality [81], fault detection [121,136], or cycle-time [170]. Classification techniques in quality control arise as a way to classify defects [83], failures in bin maps [91], or production lots [131]. The exploration of yield loss causes [84] or failure diagnostics [98] is performed using techniques as rule induction, decision trees, and association rules.

Many opportunities and improvements can still be made. For example, the semiconductor companies could employ the internet of things and sensors to empower industrial units with the capability of interpreting data and transmitting analytics, in real time, to an application that could provide insights and alerts to whom it may concern [174]. This will allow these players to gather a high amount of data. However, even though internet of things and data mining applications represent a key opportunity for semiconductor manufacturing companies—one that they should start to pursue as soon as possible, while the use of data mining in the sector is still developing under the current upgrading environment. Nevertheless, the effectiveness and scale of the internet of things implementation, and with it a comprehensive use of data mining techniques, could depend on how fast industry players can overcome some challenges [175]. In order to persevere and being able to accompany the change speed and challenges, semiconductor companies are required to adapt rapidly. Taking into account this dynamic, industrial units should embrace digitalization in an agile manner as well [176].

Limitations and Challenges

Even though employing data mining techniques has been very beneficial for this industry, as shown by all the studies used in this review, several disadvantages of data mining still exist and are as follows:

• Data mining systems can violate privacy. Absence of safety and security can be very detrimental to its users and it can create miscommunication between employees, thus leading to genuine privacy concerns [177].
• Security is an important factor related to every data-oriented technology, and semiconductor manufacturing is not an exception. Data that is very critical might be a target of malicious attacks [178].
• Too much and redundant information collection can be disadvantageous as irrelevant collected information is a challenge [179,180].
• There is a possibility of information misuse through the mining process. Data mining system have to evolve in order to diminish the misuse of the information ratio [181].
• Accuracy of data mining techniques is another limitation [182]. Accuracy is an evaluation system of measurement on how well a data mining model can perform. Many common accuracy and error scores for regression and classification can occur. Therefore, improving accuracy becomes paramount.
• Several challenges of data integration and interoperability in data mining can occur. Data interoperability and data integration affect the performance of an organization. A comprehensive approach has to be made in order to address the challenges in interoperability and integration [183,184].
• Missing and imbalanced data is a challenge in this industry. In cases in which data is imbalanced, the majority of classification algorithms have as a consequence a weak performance. Since wafer yield enhancement is a crucial performance index in semiconductor wafer manufacturing, key process steps must be cautiously selected and managed [9].
• Data processing time is another limitation that has a significant impact on the available time since data preprocessing very often involves more than 50% of time and effort of the entire data analysis process [185].

This evolution of semiconductor manufacturing relies heavily on the big data explosion in order to cope with the abovementioned data limitations and challenges of the semiconductor industry. Especially, supporting greater volumes and lengthier archives of data has allowed many solutions to correctly portray system dynamics, significantly simplify intricate multivariate interactions of parameters, eliminate disturbances, and clean and overcome data quality challenges. Data mining algorithms in such types of solutions must be rewritten in order to benefit from the parallel computation allowed by the high processing capacity and storage power with the purpose of processing data without consuming too much time. However, an enormous amount of data and a wide range of data mining techniques does not mean necessarily more predictive capability and insights [186]. Researchers and practitioners have to adapt data mining techniques in a manner so that these will be customized to specific applications in terms of data quality available data and objective, among others.

Overall, through this review, some light was shed over the possible applications of data mining techniques in semiconductor manufacturing. Yet, given the sheer number of steps that this production process has, and due to its complexity, the number of studies already made is still scarce. Big data and data mining allowed for original and innovative insights through the analysis of large amounts of data and presenting correlations and opportunities that were not previously noticed. However, decision makers must decide and which data should be collected and employed and which questions must be answered [149]. This signifies that the potential to apply these techniques in other subprocesses is enormous and is still left largely unexplored. Finally, by suffering constant and quick evolution, the need to adapt these techniques to the newer processes in semiconductor manufacturing is another opportunity to explore.

6. Conclusions

The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review was made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis was made. All data mining applications were classified in function of the application area. Five distinct areas were identified: quality control, maintenance, production, decision support systems, and finally, categorized as a whole, measurement, metrology, and instrumentation. Results showed that quality was the most popular one, with 47 publications, making 34.3% of all publications. Maintenance was an area in which only a few studies were made, highlighting the gap and the opportunity for more studies to be made in this area.

The work performed in this study concerning data mining applications in semiconductor manufacturing can have theoretical implications. The characterization and categorization of several useful and successful cases can positively contribute to future research efforts of employing such a wide range of techniques with the purpose of increasing the application and diffusion of data mining applications in semiconductor manufacturing. Knowledge of different models and algorithms could have positive implications for the development of theory, for understanding all the possible applications in different areas of semiconductor production, but also for the development of practice, since many of these were implemented and validated on the shop floor. However, as the literature review has shown, many applications can still be made since several studies address only a specific step of semiconductor manufacturing and documentation of real-life application are scarce. Additionally, recent data mining techniques and models have a great opportunity to be used since only a few studies exist. Finally, since the semiconductor manufacturing process is always evolving, the need to adapt these techniques to the newer process is another challenge and opportunity to explore.

Overall, as seen from all the comprised studies from distinct steps of semiconductor production, the scope and functions of data mining techniques can be enhanced and disseminated throughout the entire semiconductor manufacturing process in order to provide, in real time, a proactive adjustment and advanced control decisions for the whole process and the smart facilities. Therefore, more research should be made to employ and facilitate smart production for Industry 4.0 in several industries for digital transformation and for upgrading existing manufacturing units. This will allow for an improving capability for optimizing interrelated decisions and improving decision flexibility.