# 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
With increasing the demand for high-quality products and reliable processes, multivariate statistical process control (MSPC) has been developed to ensure that equipment is \"statistically controlled\" by monitoring two or more related quality characteristics simultaneously [105]. The above review indicates that AEC/APC conducts monitoring of online measurements of specific process steps, and undertakes corrective action to ensure that the parameter being measured remains within the desired limits. However, the integration of FDC, R2R, SPC, and VM has been considered only in a few research papers [86], requiring further research from different aspects such as the consistency and integration of data, unified frameworks, high-efficiency algorithms and platforms, and so on. The application of DM with Big Data for quality improvement of electronic products, especially for wafer fab at the production stage was summarized in Table 12. One of the research papers deals with predicting the performance (yield) of a manufacturing process or system in terms of critical functional characteristics. Months may pass before a chip is completed; hence, there is a great interest in mining production data to predict its performance prior to the final testing of the wafers [100; 101; 102; 103; 104; 105; 106; 107; 108]. In order to infer to the possible causes of faults and manufacturing process variations in semiconductor manufacturing after the whole fab process is completed, the clustering, classification, and association analyses are conducted based on different DMTs such as k-means, SOM, SVM, and decision tree to identify critical poor yield factors and determine the root cause of low yield. On this basis, the related process parameters can be adjusted to ensure future quality based on post hoc diagnosis [110; 111; 112; 113; 114; 115; 116; 117; 118]. Some studies combined with sequential pattern mining to identify the sequence association events between different operations during the manufacturing [119; 120]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Functions (Frequency)** & **DMTs** & **Categories** &
\begin{tabular}{c} **Knowledge** \\ **Area/Task** \\ \end{tabular} & **Indicator** & **Ref.** \\ \hline \multirow{8}{*}{Prediction (9)} & FNN & KD-oriented & & MAE, RMSE, \\  & Regression, ANN, & & & MAPE \\  & K-means clustering & SA-oriented & & R\({}^{2}\) & [101] \\  & PLS, CART & & & prediction & MAPE \\  & Generalized linear & & & MSE & [103] \\  & mixed models & & & MAE, RMSE, \\  & FNN & KD-oriented & & MAPE \\  & FCM + GA + DT & & Quality & ME \\  & Fuzzy linear & & & \\  & regression + BPN & SA-oriented, & & Cost prediction & RMSE, MAE, \\  & & KD-oriented & & MAPE \\  & Regression, ANN & & Yield & MAE, MAPE, \\  & & prediction & R\({}^{2}\) \\  & FNN & KD-oriented & Yield & MAE, RMSE, \\  & & prediction & MAPE \\ \hline \end{tabular}
\end{table} 
Table 12: DM with Big Data application for the quality improvement at the production stage. Moreover, more than hundred test items and millions of rows of data for wafers will be generated after testing, per day. According to the basic requirements of quality management, an essential work is to analyze these test items one by one according to different specifications and requirements. In accordance with the traditional mode of work, more than a hundred process capability indexes should be calculated step by step and the quality characteristics should be evaluated one by one with enormous and complicated operations. Meanwhile, it is difficult to determine the association between these indexes and present a comprehensive summary of the overall performance of the product. The application of Big Data for the quality management and analysis can easily generate a traditional single index process capability analysis report. More importantly, it can excavate many new results from the Big Data set [114].