# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 1 Introduction
This method outperformed traditional approaches on real datasets, significantly improving AUC scores, Geometric Mean, and F1 scores. [PERSON] [30] introduced a Coordinated CNN-LSTM-Attention model to effectively capture semantic and emotional dependencies in text, enhancing text sentiment classification by adaptively encoding semantic and emotional information. Experimental results showed superior performance in capturing local and long-distance semantic-emotional information compared to advanced benchmarks. [PERSON] [31] proposed a CNN-LSTM-attention method for missing log curve prediction, effectively capturing geological features of raw curves and improving log interpretation reliability. [PERSON] [32] developed a CNN-LSTM short-term power load forecasting model, significantly improving prediction accuracy through optimized information extraction and weighting. [PERSON] [33] proposed a CNN-LSTM method for electrical machine fault detection, monitoring vibration data via sensors and applying quantile regression to manage data uncertainty, achieving effective prediction of potential anomalies. [PERSON] [34] introduced a CNN-LSTM architecture for indoor temperature prediction, combining convolutional feature extraction with LSTM's sequence dependency learning, significantly enhancing long-term prediction accuracy and robustness. [PERSON] [35] proposed a Parallel CNN-LSTM Attention (PCLA)model for regional heating load forecasting, achieving significant improvements in accuracy in terms of mean squared error and R2 values. In summary, the analysis of WAT yield prediction and model construction provides valuable insights for this study's research on RFFE mass production yield prediction. Building on prior related research, this study aims to further explore and refine these approaches. Therefore, based on the testing requirements of RFFE chips, this paper extracts common testing rules to significantly shorten the development time of testing solutions and system deployment for such chips. It also optimizes testing solutions and processes to improve chip testing efficiency, achieving cost reduction and efficiency gains to meet tight product launch timelines. By applying machine learning techniques to RFFE final test yield prediction, this study fills a research gap in the field, contributing to the healthy development and technological advancement of the RF chip industry. The main contributions of this paper are summarized as follows:  
1. This study introduces a test parameter grouping strategy based on RFFE device correlations, grouping metrics like input/output power, gain, and Power Added Efficiency (PAE), with PAE as a representative indicator of power amplifier (PA) linearity impacting Adjacent Channel Leakage Ratio (ACLR) and Error Vector Magnitude (EVM). Using single signal configuration, it reduces test time and errors, enhancing efficiency and reliability in mass production testing. 2. This study develops a submodel framework for analyzing historical yield data. Specifically, Model A employs CNN-LSTM architecture to extract local features and capture long-term trends, thereby enhancing the accuracy of yield predictions; Model B fuses multidimensional test features via multichannel inputs to reveal intrinsic yield drives; Model C employs adaptive sliding window averaging with dynamic weighting to smooth short-term noise, offering a stable baseline. This multiperspective approach leverages temporal and feature diversity, enhancing prediction robustness. 3. This study designs a dynamic weighted fusion mechanism integrating three submodel outputs via learnable weights, coupled with incremental training (Monte Carlo uncertainty and adaptive learning rates), forming a robust model. Cross-constraint loss ensures feature consistency, improving RFFE final test yield prediction accuracy.