# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 2 Materials and Methods
### Establishment of Yield Prediction Submodel  
During the final measurement and mass production stages of chips, yield variations are influenced by multiple factors, and the test data exhibit multidimensional inputs and temporal characteristics. Therefore, deep learning models are employed to train and analyze large volumes of test data, aiming to uncover inherent patterns and achieve accurate predictions of chip yield. In this study, a submodel combining CNN-LSTM with sliding window average prediction validation was designed and constructed. By integrating multiple input strategies, this model fully exploits temporal and multidimensional feature information within the data, thereby enhancing prediction performance. (1) Model A: A CNN-LSTM model for historical yield prediction. Construct a historical yield prediction Model A based on CNN-LSTM. In the mass production testing of RFFE RF chips, historical yield data often exhibit significant nonlinear fluctuations and local anomalies, which are closely related to short-term variations in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Batch** & **OS Test** & **Leakage** & **PAE\_GSM915M** & **PAE\_GSM1910M** & **Insertion** & **Yield** \\  & & **Current** & & & **Loss** & **Yield** \\ \hline
0.001 & 0.000 & 0.006 & 0.006 & 0.004 & 0.006 & 0.9720 \\
0.001 & 0.000 & 0.006 & 0.008 & 0.008 & 0.014 & 0.9600 \\... &... &... &... &... &... &... \\
0.63 & 0.000 & 0.000 & 0.002 & 0.002 & 0.002 & 0.9780 \\
0.63 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.9725 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RFFE mass production test dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Items** & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** & **Accumulative** \\  & & & & & & **Total** \\ \hline OS test & 0.41 & 0.36 & 17.13 & 18.94 & 16.48 & 53.32 \\ Leakage & 47.19 & 51.46 & 0.22 & 0.15 & 0.23 & 99.25 \\ current & & & & & & \\ PAE\_GSM915M & 50.04 & 42.61 & 0.54 & 0.33 & 0.51 & 94.03 \\ PAE\_GSM824M & 2.32 & 5.41 & 10.27 & 3.88 & 7.64 & 29.52 \\ PAE\_8 PSK915M & 0.01 & 0.15 & 11.9 & 2.29 & 11.44 & 25.79 \\ PAE\_GSM1910M & 0 & 0 & 27.65 & 7.46 & 61.95 & 97.06 \\ PAE\_8 PSK1910M & 0.02 & 0 & 8.60 & 4.17 & 0.51 & 13.3 \\ Insertion loss & 0 & 0 & 23.66 & 62.76 & 1.23 & 87.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical table of the top five principal components. different batches of chips and manufacturing process parameters. Model A first utilizes the CNN module, employing input layers, convolutional layers, and pooling layers to extract local features from historical yield data, effectively capturing short-term yield changes caused by sudden shifts in process parameters (such as OS anomalies or PAE deviations). The extracted features are then fed into the LSTM layer, which leverages its input gate, forget gate, and output gate to establish a temporal memory mechanism, modeling global trends arising from factors like process drift, temperature fluctuations, and equipment instability over long periods. This approach compensates for the limitations of single models in deeply mining long-term sequential information. During the final measurement and mass production stages of chips, yield variations are influenced by multiple factors, and the test data exhibit multidimensional inputs and temporal characteristics. Therefore, deep learning models are employed to train and analyze large volumes of test data, aiming to uncover inherent patterns and achieve accurate predictions of chip yield. In this study, a submodel combining CNN-LSTM with sliding window average prediction validation was designed and constructed. By integrating multiple input strategies, this model fully exploits temporal and multidimensional feature information within the data, thereby enhancing prediction performance. The CNN model ultimately outputs to the input layer of the LSTM to capture timing-dependent data.