# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 1 Introduction  
The Radio Frequency Front-End, as a core component of modern wireless communication systems, plays a critical role in signal transmission, reception, and processing. With the rapid development of 5G technology [1], the Internet of Things (IoT) [2], and smart devices [3], the performance of RFFE directly determines communication quality, data transmission rates, and the overall reliability of devices [4]. For instance, in 5G communication, RFFE must support higher frequency ranges, wider bandwidths, and lowerpower consumption to meet the demands of multiantenna technology and millimeter-wave communication [5]. Furthermore, RFFE modules are widely applied in fields such as smartphones, base stations, automotive electronics, and industrial equipment, with their market size and technical complexity continuously growing [6]. Consequently, the research, development, and manufacturing of RFFE are not only key drivers of advancements in communication technology but also a vital foundation for upgrading related industry chains. In the RFFE manufacturing process, the final test stage is a critical step to ensure that product quality and performance meet specification requirements. However, due to the involvement of multilayer processes, high-precision integration, and complex electrical characteristics in RFFE devices, the production process is inevitably affected by fluctuations in raw materials, equipment deviations, and environmental factors, leading to variations in yield. Low yield not only increases production costs, but may also delay delivery schedules and even impact a company's competitiveness. For chip manufacturers, time is a competitive asset, particularly for complex chips like RFFE, where excessively long product development, production, and testing cycles are detrimental to market competition [7]. Thus, accurately predicting the yield of RFFE final testing has become a core requirement for manufacturing optimization and cost control [8]. Traditional yield management methods, which often rely on human experience or simple statistical analysis, struggle to adapt to the large data volumes and multidimensional variables of modern semiconductor manufacturing [9]. By introducing advanced predictive techniques, potential issues can be identified early in production, process parameters can be optimized, and yield can be improved, reducing scrap rates and achieving greater economic benefits. For the mass production testing of RFFE chips, the industry predominantly adopts a scheme that integrates Automated Test Equipment (ATE) as the core with other testing resources to form a comprehensive test system environment. Given that China's 5G deployment primarily operates in the sub-6 GHz frequency band, the mass production testing technology for RFFE chips in this band has reached maturity. Consequently, the RFFE chip testing items in this study focus on the Frequency Range 1 (FR1) frequency range. In the future, testing is expected to gradually shift from lower frequencies to encompass both low-frequency and high-frequency modes, such as millimeter-wave bands. The millimeter-wave spectrum offers abundant bandwidth resources, significantly enhancing communication speeds and enabling an optimal 5G experience. [PERSON] discussed the application scenarios of 5G millimeter-wave technology (5G NR FR2), global market trends, device trends, and the associated testing challenges for such devices, alongside mass production testing solutions implemented on the Advantest V93000 platform [10]. In the context of ultra-wideband (UWB) technology, higher demands are placed on ATE systems. [PERSON] explores how the V93000 ATE testing solution for wideband RF applications addresses the growing needs of this market. However, higher frequencies may introduce new effects on RF components, potentially further impacting device yield [11].