# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 2 Materials and Methods
### Establishment of the Dataset
#### 2.1.2 Establishing the Dataset Based on PCA  
Next, PCA algorithm will be used to extract the principal components representing the data information, so as to select the data with a high contribution rate to the selected principal components, and select a reasonable dataset to improve the evaluation performance of the model.  
This study relies on the RFFE mass production test plant project, which involved testing a current 5G RFFE chip, collecting test data from more than 24 million chips, and merging them. Among them, the test time of single chip is about 860 ms, the material transfer time is 300-400 ms, and the comprehensive test time of single chip is about 1.2 s. Considering the actual situation of the factory, under the comprehensive requirements of relatively stable yield change and fault response time, the real-time yield was calculated at 10 minutes, that is, every 500 chips in the chip test were grouped, and a total of 48,609 sets of data were collected.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Test Item** & **VIF** & **Test Item** & **VIF** \\ \hline OS test & 1.138 & PAE\_gPSK915M & 4.964 \\ Leakage current & 1.195 & PAE\_GSM1910M & 6.156 \\ PAE\_GSM915M & 8.194 & PAE\_8 PSK1910M & 7.716 \\ PAE\_GSM824M & 12.113 & Insertion loss & 2.157 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics of collinearity.  
In total, 48,609 sets of data of 8 test items were analyzed by PCA analysis to calculate the cumulative variance contribution, as shown in Figure 3. When the cumulative variance contribution rate exceeds 95%, these elements can be considered to have captured the core information of the original data.  
According to the figure, the cumulative contribution rate of the top five factors has reached 95%, so it is decided to select the top five main components. Each principal component is a linear combination of the original features, and different test items will have different weighting coefficients for different principal components. Therefore, the contribution rate of each test item to the top five principal components was counted, as shown in Figure 4.  
The leakage current, PAE_GSM915M, PAE_GSM1910M, and insertion loss in the test items contribute greatly to each autonomous component, indicating the strong ability of these test items to explain this principal component. The specific calculation results of the  
Figure 4: Heatmap of the contribution rate of each test item to the top five principal components.  
Figure 3: Contributions rate and cumulative contribution rate of principal components.  
contribution rate to the top five principal components and the cumulative contribution rate are shown in Table 3.  
Although the PAE_GSM824M, PAE_8 PSK915M, and PAE_8 PSK1910M have some impact on the final yield, the contribution of the five principal components is not high, all more than 30%, indicating that their effect on the yield is relatively small or local.  
In conclusion, the five test items were selected as characteristic inputs of the model, including the OS test, leakage current, PAE_GSM915M, PAE_GSM1910M, and insertion loss. In addition, batches are also the key indicator affecting the yield. There are certain differences between different batches of chips in the same type, so it is also necessary to classify different batches of chips and be calibrated with different numbers between 0 and 1. Finally, a dataset of batches, five test item failures, and the final yield was formed, as shown in Table 4 below.