# 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
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Functions** \\ **(Frequency)** \\ \end{tabular} & **DMTs** & **Categories** & \begin{tabular}{c} **Knowledge** \\ **Area/Task** \\ \end{tabular} & **Indicator** & **Ref.** \\ \hline \multirow{8}{*}{Prediction (7)} & ANN & KD-oriented & \begin{tabular}{c} Allocation of \\ resource \\ \end{tabular} & MSE, MAPE & [43] \\  & Regression & SA-oriented & Cycle time & MAE, MSE & [44] \\  & FNN & KD-oriented & Prediction & RMSE, MAE, RMSE & [45,46] \\  & FNN & KD-oriented & Rescheduling & RMSE & [47] \\  & GNR, ANN & KD-oriented, SA-oriented & Cycle time & MAPE & [48] \\  & ANN & KD-oriented & Assembly & MSE, MAE, \\  & & & times perdition & \begin{tabular}{c} MSE, MAE, \\ MSE, RAE \\ \end{tabular} & [49] \\ \hline \multirow{8}{*}{Classification (6)} & FACRs (Apriori + & \multirow{2}{*}{KD-oriented} & \multirow{2}{*}{Scheduling} & - & [50] \\  & Fuzzy logic) & & & & \\ \cline{1-1}  & FNN + ANN + Apriori & KD-oriented & \multirow{2}{*}{\begin{tabular}{c} Cycle time \\ prediction \\ \end{tabular} } & - & [51] \\ \cline{1-1}  & DT, ANN & KD-oriented & \multirow{2}{*}{\begin{tabular}{c} Cycle time \\ prediction \\ \end{tabular} } & ACC & [52] \\ \cline{1-1}  & SNBC & SA-oriented & \multirow{2}{*}{\begin{tabular}{c} Human \\ management \\ \end{tabular} } & ACC & [53] \\ \cline{1-1}  & SVM, RST, DT & KD-oriented & \multirow{2}{*}{\begin{tabular}{c} Human \\ management \\ \end{tabular} } & ACC & [54] \\ \cline{1-1}  & GA + SVM & KD-oriented & Scheduling & - & [55] \\ \hline \multirow{8}{*}{Prediction (23)} & FCM + FNN & KD-oriented, SA-oriented & \multirow{2}{*}{\begin{tabular}{c} Scheduling \\ DBI \\ \end{tabular} } & - & [56] \\  & SVM + FNN & KD-oriented & \multirow{2}{*}{\begin{tabular}{c} MAE, MAPE, \\ RMSE \\ \end{tabular} } & [57] \\ \cline{1-1}  & SVM + ANN & KD-oriented & \multirow{2}{*}{\
\begin{tabular}{c} Scheduling \\ DBI \\ \end{tabular} } & - & [58] \\ \cline{1-1}  & FCM + ANN & KD-oriented, SA-oriented & \multirow{2}{*}{
\begin{tabular}{c} RMSE, MAPE \\ DBI \\ \end{tabular} } & [60] \\ \cline{1-1}  & SOM+ FNN & KD-oriented & & RMSE & [62] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: DM with Big Data application for production management and optimization. Tens of thousands of monitoring and online detection measurement values, and hundreds of electrical test parameters timely measured at different positions on a wafer during the fab process facilitates the Big Data application for production control. The typical knowledge area of these applications is AEC/APC that is a collection of tasks including FDC, R2R control, SPC, and VM to reduce the process variation and meet the process target for yield (quality) enhancement. The related literature is summarized in Table 11 from which we can see that the outlier detection was conducted online and the time series analysis was employed for anomaly detection while the prediction function was mainly used for VM and R2R. Classification and clustering have been widely used for FDC. Some preprocessing like regression [87; 88; 89] was conducted to identify the main effects on observation variables before classification or clustering model establishment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
* FACRs: Fuzzy association classification rules; GNR: Gauss-Newton regression; RBFNN: Radial basis function neural network.  
\end{table} 
Table 11: DM with Big Data application for advanced equipment control/advanced process control. The data-driven mechanism is one of the pervasive approaches to FDC [114] and the summarization in Table 11 also indicates that FDC (or FD only) is the most researched task of AEC/APC [84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 95; 97; 98; 99].