# A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
## 4 Data Mining with Big Data Applications in the Electronics Industry
### Application of DM with Big Data in Different Stages
#### 4.2.2 Application of DM and Big Data for Production
The wafer fab is a complex and lengthy process that involves hundreds of process steps, and early FD gives engineers more time to perform appropriately to avoid serious equipment abnormalities [84; 85; 86] while fault classification can be considered as the combination of fault identification and diagnosis in order to identify the main effects on observation variables, concentrate on the process variables related to diagnosing abnormalities, and then to determine the cause of the observed out-of-control status that can facilitate the process recovery by removing the cause of the fault to reduce yield loss [86; 90]. R2R control consists of several levels including real-time control, single-process R2R control, inter-process R2R control, and factory-level R2R [79]. FDC stands for a representative technique of real-time control. Single-process R2R control focuses on an individual process module while the selected R2R related articles concentrate mainly on inter-process R2R that deals with the process control of two or more inter-related process modules [80; 86] combined with other tasks like FDC. The factory-level R2R has only been considered by a few research papers [79] that are used to enhance the results of electronic tests in wafer acceptance tests and yield circuit probe tests. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Functions (Frequency)** & **DMTs** & **Categories** &
\begin{tabular}{c} **Knowledge** \\ **Area/Task** \\ \end{tabular} & **Indicator** & **Ref.** \\ \hline \multirow{8}{*}{Classification (12)} & SVM & KD-oriented & FDC & ROC & [84] \\  & Logistic regression & SA-oriented & FDC & TPR & [85] \\  & A framework & SA-oriented, KD-oriented & FDC, R2R, SPC, EHM & ACC & [86] \\  & Forward stepwise & & & & \\  & regression, LASSO, & & & & \\  & Random forest & SA-oriented & FDC & R2 & [87] \\  & MLR & & & & \\  & Stepwise regression, & & & & \\  & CART & SA-oriented, KD-oriented & FDC & ACC & [90] \\  & K-means, CHAID & & & & \\  & PCA, SOM, CHAID & & & TPR, TNR & [91] \\  & Multi-sensor-based trace segmentation, PCA & SA-oriented & FDC & ACC & [92] \\  & Spatial statistics, ANN & KD-oriented, SA-oriented & FDC, SPC & - & [93] \\  & SOM, K-means, DT & SA-oriented & SPC & DBI & [94] \\  & CART & KD-oriented & FDC & - & [95] \\ \hline \multirow{4}{*}{Clustering (4)} & K-means, CHAID & & FDC & ACC & [90] \\  & PCA, SOM, CHAID & SA-oriented & FDC & - & [91] \\  & SOM, K-means, DT & KD-oriented & SPC & DBI & [94] \\  & SDC & SPC & FAR, FRR & [96] \\ \hline \multirow{4}{*}{Time series analysis (3)} & CART & KD-oriented & FD & - & [95] \\  & osPCA, online PCA, ABOD, LB-ABOD, LOF & & & \\  & EBIT, CUSUM & SA-oriented & FD & PPV, TPR & [97] \\ \hline \multirow{4}{*}{Outlier detection (3)} & osPCA, onlinePCA, BOD, LB-ABOD, & & & \\  & LOF & SA-oriented & & & \\  & EBIT, CUSUM & FD & TP, FN & [98] \\  & PSLA & FD & - & [99] \\ \hline \end{tabular}
\end{table} 
Table 11: _Cont._The reviewed VM-related literature utilized MLR [80], ANN [81], SVM(R), and DT [82; 83] based on the production equipment data and preceding metrology results to predict every wafer's metrology measurements, which fills a lack of physical measurement by prediction that enables the measurement of every wafer for every process step on all capable equipment available in the fab, thus, allowing significant improvement of process control and product quality, reduction of operational cost, and production cycle time [81; 83]. In SPC, significant characteristics are monitored such as the failure percentage of wafer bin maps [93] and the soldering quality [94]. The process control chart, as a widely used approach to SPC [93; 94], has been used to diagnose and identify the variability of the fab process. The statistical process system can help detect defects that might originate from the process steps to improve quality and eliminate the need for expensive post inspections [94; 96].