# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 1 Introduction
[PERSON] analyzed the effects of RF and channel impairments on the performance of ultra-wideband wireless orthogonal frequency-division multiplexing (OFDM) systems based on the IEEE 802.15.3c standard [12]. The use of OFDM at UWB operating frequencies is significantly affected by the nonlinearity of the RF front-end [13]. [PERSON] provided a comprehensive analysis of nonidealities in ultra-wideband systems from both component and system perspectives. The behavior of real UWB transmission scenarios is investigated through simulations and measurements of manufactured components [14]. As chip production scales continue to expand, effectively predicting and improving RFFE yield has become an urgent challenge for manufacturers. RFFE yield prediction presents significant uniqueness and technical challenges. First, RFFE production data typically exhibit temporal and nonlinear characteristics. Test data vary over time and are influenced by interactions between process steps, making it difficult for traditional static models to capture these dynamic patterns [15]. Second, RFFE yield is affected by multi-source heterogeneous data, including electrical parameters, environmental variables, and equipment conditions, making effective data fusion a key aspect of predictive model design [16]. Additionally, yield prediction must balance short-term fluctuations and long-term trends, being sensitive to anomalies while avoiding overfitting to noisy data. This requires prediction methods to strike a balance between feature extraction, model robustness, and computational efficiency. Traditional yield prediction methods often rely on empirical judgment and simple statistical analysis, which are inadequate for handling complex, variable production environments and vast amounts of test data. This results in insufficient prediction accuracy, subsequently affecting production costs and product quality. In recent years, machine learning and deep learning techniques, such as convolutional neural networks and long short-term memory networks, have demonstrated significant potential in handling complex time-series data [17; 18]. However, a single model struggles to fully address the multidimensional nature of RFFE yield prediction, necessitating the exploration of multimodal fusion and dynamic weighting strategies to enhance prediction accuracy and adaptability. Current research on chip test yield prediction has largely focused on the wafer testing stage, primarily due to the traceability of test data in Wafer Acceptance Testing (WAT). WAT benefits from relative coordinates being recorded in data files and the presence of adjacency effects during manufacturing [19]. In contrast, during the final test stage, the chips are already packaged and fed through vibrating bowls, introducing randomness that eliminates the concept of coordinates. Moreover, the final testing is comprehensive, involving more test items, and customers place greater emphasis on data security, resulting in limited resources available for research in this area. To date, researchers have conducted extensive studies on wafer yield prediction, primarily focusing on two aspects:  
1. Feature Selection Methods. Addressing the challenges of high-dimensional WAT data, complex parameter relationships, and unclear key parameters, researchers aim to develop efficient methods for selecting critical parameters. 2. Prediction Models. To explore the complex relationships between WAT parameters and wafer yield, as well as the temporal correlations in data, high-precision prediction models are designed. These models aim to reduce the workload of WAT parameter adjustments, accurately predict wafer yield, and enable in-depth analysis of factors causing quality degradation during production, thereby improving wafer yield rates. These research approaches can also be adapted for yield prediction at the final test stage.