# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 4 Conclusions  
In semiconductor manufacturing, the final testing phase of RFFE chips is pivotal for ensuring product quality and optimizing production efficiency. This study addresses the critical need for accurate yield prediction at this stage, where traditional methods and prior research, predominantly focused on wafer-level analysis, fall short in tackling the unique challenges of final testing--such as test condition variability and complex failure patterns. To this end, we proposed a novel multistrategy dynamic weighted fusion model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and sliding window averaging, tailored specifically for RFFE mass production testing. The methodology leverages a three-pronged approach. First, a rigorous RF correlation parameter selection process, employing [PERSON]'s correlation coefficient and variance inflation factors (VIF), identified key test metrics--such as multifrequency PAE, OS test, leakage current, and insertion loss--mitigating multicollinearity via Principal Component Analysis (PCA) to construct an optimized dataset from over 24 million RFFE chip test records. Second, three submodels were developed: submodel A (CNN-LSTM) captures historical yield trends, submodel B integrates multidimensional features for intrinsic yield insights, and submodel C employs adaptive sliding window averaging to provide stable baselines, collectively enhancing prediction diversity. Third, a dynamic weighted fusion mechanism, augmented by incremental training with Monte Carlo uncertainty quantification and adaptive learning rates, integrates these submodels, ensuring adaptability to production dynamics. Experimental evaluation demonstrated the model's superior performance. With optimized parameters--including Tanh activation, a batch size of 25, 20 LSTM units, and a dropout rate of 0.2--the fusion model achieved a Mean Absolute Error (MAE) of 0.84% and a Root Mean Square Error (RMSE) of 1.24% on a consistent dataset. Compared to static and single submodel predictions, the dynamic fusion model reduced MAE by up to 7.69% and RMSE by up to 13.29%, underscoring its enhanced accuracy and reliability. The Tanh activation function's zero-centered properties and the multiperspective submodel design proved instrumental in capturing complex data patterns, while incremental training endowed the model with robust adaptability to real-time production shifts. This study's contributions are threefold: it introduces an efficient test parameter grouping strategy based on RF device correlations, develops a versatile submodel framework leveraging temporal and feature diversity, and establishes a dynamic fusion model with incremental training for precise yield forecasting. These advancements not only fill a research gap in RFFE final test yield prediction but also offer practical benefits, such as reduced testing time, improved process optimization, and enhanced cost-efficiency, aligning with the stringent timelines of modern chip manufacturing. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **MAE/\%** & **RMSE/\%** \\ \hline \multirow{2}{*}{Submodel A} & Static forecasting & 0.91 & 1.36 \\  & Dynamic & 0.86 & 1.36 \\  & forecasting & 0.91 & 1.43 \\ \hline \multirow{2}{*}{Submodel B} & Static forecasting & 0.87 & 1.37 \\  & forecasting & 0.86 & 1.36 \\ \hline \multirow{2}{*}{Fusion model} & Static forecasting & 0.84 & 1.24 \\  & forecasting & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of prediction accuracy among different models. Despite these achievements, limitations remain. The computational complexity of incremental training may pose challenges for real-time deployment in resource-constrained environments. Future work could explore lightweight optimization techniques, such as model pruning or quantization, to enhance efficiency without sacrificing accuracy. Additionally, extending the model to incorporate multistage production data or adapting it to other semiconductor devices could further broaden its applicability.