The fatigue life of solder balls in Ball Grid Array (BGA) packages of a Printed Circuit Board (PCB) assembly is a key factor that influences the reliability of electronic devices. This study explores three novel methods to enhance the fatigue life of the PCB assembly with BGA packages, focusing on stress reduction techniques in solder balls under random vibration.The numerical analysis of the PCB assembly examined stress distributions and identified optimal design strategies to improve resistance to fatigue under random vibration. The findings demonstrate that strategic positioning of BGA packages, the use of additional supports, and modifications to PCB assembly can significantly reduce stress on solder balls, thus extending their fatigue life 8 to 10 times that of the existing design.
With the growing demand for high-safety and high-energy-density energy storage systems, all-solid-state lithium batteries (ASSLBs) have attracted extensive attention due to their excellent safety and potential high performance. Among them, the solid electrolyte is a key component. This study systematically investigates the effects of Li3AlF6 as a sintering aid on the sintering behavior, crystal structure, microstructure, and electrochemical performance of LLNO (Li0.25La0.25NbO3) ceramics. LLNO ceramic samples with different Li3AlF6 doping levels (0-8 wt.%) were prepared via a solid-state reaction method and sintered at 1050 degrees C and 1100 degrees C. The samples were characterized by XRD, SEM, AC impedance spectroscopy, and DC polarization testing. The results indicate that Li3AlF6 significantly reduces the sintering temperature of LLNO and promotes the densification process. However, excessive doping (8 wt.%) leads to the formation of impurity phases such as AlF3, increases grain boundary resistance, and reduces ionic conductivity. The sample with 5 wt.% Li3AlF6 sintered at 1050 degrees C exhibits the best overall performance: an ionic conductivity of 2.08 & times; 10(-6) S & centerdot;cm(-1) and an activation energy of 0.340 eV. Although its conductivity is slightly lower than that of pure LLNO (4.12 & times; 10(-6) S & centerdot;cm(-1)) sintered at 1100 degrees C, the introduction of Li3AlF6 provides a feasible route for low-temperature sintering, which is beneficial for future co-sintering integration with electrode materials.
The present work investigates the long-term relaxation behavior of anomalous photovoltage and photoelectret voltage in polycrystalline CdTe:Ag thin films prepared by oblique thermal evaporation.The relaxation kinetics were studied at room temperature under controlled photopolarization conditions. Experimental results reveal a pronounced two-stage relaxation behavior, consisting of a fast initial component with characteristic times of 2-4 min and a slow long-term component extending up to 25-45 min. The short-term relaxation is attributed to shallow trapping centers located near the band edges, while the long-term relaxation is governed by deep trapping centers associated with impurity-defect complexes involving silver atoms and cadmium vacancies. The observed metastable photoelectric states are explained within a bulk trapping model combined with near-surface band bending effects, which lead to asymmetric photocarrier separation and long-lived charge retention. The obtained results provide new insight into defect-controlled relaxation processes in CdTe:Ag thin films and demonstrate the crucial role of deep trapping centers in the formation and stability of photoinduced electric states. These findings are relevant for optimizing the photoelectric performance and long-term stability of CdTe-based photovoltaic and optoelectronic devices.
Phase-Frequency Detector (PFD) and Charge Pump (CP) are crucial components in Phase-Locked Loops (PLL) and Delay-Locked Loops (DLL), significantly impacting synchronization accuracy and system stability. In this paper, a new PFD based on dynamic logic with a minimal number of devices is designed, providing low delay and proper logic output levels. Additionally, a novel CP employing the gain-boosting technique and utilizing a cascode amplifier is proposed, achieving higher DC-gain and better current matching.The proposed design aims to minimize power consumption to the microwatt level, reduce circuit area, and achieve an operating frequency up to 3 GHz. Simulation results in 180-nm CMOS technology using Cadence software demonstrate that the proposed circuit offers lower power consumption, smaller area, and improved dynamic performance compared to conventional designs, making it well-suited for PLL and DLL applications in high-frequency communication systems.
Active noise cancellation (ANC) is an essential feature of audio equipment that reduces unwanted background noise. Unwanted signals in information bearing-signal referred to as noise, could degrade the strength of signals in terms of intelligibility and quality. Over the decade, various researchers developed different algorithms to enhance speech signal quality and for noise reduction. To address the issue, a Multitude Active Noise cancellation using White Shark Optimized Convolutional neural network-Long short-term memory Network (MANC Net) has been proposed. Initially, Dual Tree Complex Wavelet Transform (DTCWT) is utilized to enhance the quality of audio signal with a multitude noise and the signal features are extracted using a community detection based Genetic Algorithm. Afterward based on extracted signal, interference and desired signals are classified using Hybrid Convolutional neural network-Long short-term memory (CNN-LSTM). Additionally, the hyperparameters of CNN-LSTM are tuned using White Shark Optimization (WSO) for better accuracy. The efficiency of the proposed method is evaluated using accuracy, specificity, sensitivity, Normalized Mean Squared Error (NMSE), Short-Time Objective Intelligibility (STOI), and Perceptual Evaluation of Speech Quality (PESQ) parameter values in comparison with other conventional methods. The higher accuracy rate and low NMSE in the classification of audio signals evidenced the efficacy of the proposed MANC Net model. The overall accuracy of the proposed is 9.1%, 8.7%, 7.9%, 3.4%, and 1.5% better than Filtered-X Least Mean Square (FxLMS), deep Active Noise Cancellation (deep ANC), Construction Site Noise Network (CsNNet), Multi-Channel Active Noise Cancellation (MCANC), and Generative fixed-Filter Active Noise Control (GFANC), respectively.
