# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 2 Materials and Methods
### Establishment of the Dataset
#### 2.1.1 Correlation Analysis of Feature Testing Items  
Following data extraction and analysis, we can quantify the interaction between the feature test terms, as shown in Figure 2, to assess their combined effect on yield. Pearson correlation coefficient analysis reveals the correlation between the selected input dataset and the construction of the deep learning model [37].  
The calculated expression is described in Equation (2):  
\[r=\frac{\sum\left(X-\overline{X}\right)\left(Y-\overline{Y}\right)}{\sqrt{ \sum(X-\overline{X})^{2}\left(Y-\overline{Y}\right)^{2}}} \tag{2}\]  
where \(X\) and \(Y\) both represent different test item data. The value of the correlation coefficient \(r\) is calculated, where 1 indicates a complete positive correlation, while 0 indicates no linear correlation.  
As can be seen in Figure 2, there is a strong correlation between the RF performance test items, with \(r\) exceeding 0.5. The occurrence of this situation is a normal phenomenon, which also exactly reflects the internal structure characteristics of the RFFE chip. Because the transmission of different frequency point signals is completed by PA, LNA, and other devices inside the chip, the manufacturing defects will affect the transmission and receiving of all frequency point signals. Therefore, the test results of the same test items at different frequency points will generally tend to change in the same trend (high or low), and their correlation is naturally strong. Therefore, PAE_SM915M, PAE_GSM824M, PAE_8 PSK915M,  
Figure 2: Thermal map of the correlation coefficient.  
PAE_GSM1910M, and PAE_GSM1910M are not suitable for all the features of the data because of their strong correlation, and subsequent analysis is needed.  
All data processing and visualization tasks in this study, including the generation of Figure 2 and subsequent data plotting, were conducted using Python 3.9 within the PyCharm Community Edition 2022.3.3 integrated development environment. Key libraries such as NumPy (1.26.4), Pandas (2.2.2), scikit-learn (1.5.1), and Matplotlib (3.9.0) were utilized for numerical computation, data analysis, machine learning implementation, and plotting, respectively.  
Variance inflation factor (VIF) is commonly used to assess the degree of correlation between variables [38]. When VIF values exceed the threshold of 5, it indicates potential multicollinearity. The calculation formula is given in Equation (3):  
\[VIF_{n}=\frac{1}{1-{R_{n}}^{2}} \tag{3}\]  
where \(n=1\), \(2\),..., \(j-1\), \(j\) refers to the number of influencing factors, \(R_{n}\) is the multiple correlation coefficient, which describes the strength of the correlation, and \({R_{n}}^{2}\), the coefficient of determination in the regression model, represents the value obtained from an auxiliary regression where the nth independent variable is regressed on all other independent variables. In this auxiliary regression, the nth independent variable serves as the dependent variable, while the remaining independent variables serve as the independent variables. The value of Rn\({}^{2}\) ranges from 0 to 1. The VIF calculation of the above involved test items was performed to analyze their internal correlation degree, and the results are shown in Table 2.  
From this perspective, the VIF values of four test items have exceeded the conventional limits, and there is multicollinearity among variables. It is not appropriate to use these factors directly for model construction and yield prediction. Therefore, it is necessary to use PCA algorithm to compress the data dimension, deal with the above multicollinearity problems, and screen the data, so as to determine the data suitable for yield evaluation.