In recent years, with the emergence of UAVs(Unmanned Aerial Vehicles) in military and civil applications, the FANETs(Flying Ad-Hoc Networks) composed of multiple UAVs has attracted extensive attention from researchers. As a new type of airborne self-organizing network, the particularity in FANETs such as time-varying network topology and dynamic link makes it difficult to maintain continuous communication when performing tasks. Therefore, it is challenging to design a routing protocol for FANETs to guarantee the quality of data transmission and make communication more effective. In this article, we propose a new opportunistic routing protocol based on trajectory prediction, named EORB-TP. To be specific, we first predict the position of nodes in three-dimensional space and solve the problem of uncertainty of node contact in opportunistic communication. Secondly, we define the node's trajectory metric value to measure the node's trajectory characteristics and effectively avoid the excessive consumption of edge nodes. In addition, when choosing relay nodes, an energy-saving data forwarding strategy is designed to deal with the limited energy resources and storage space of UAVs. Simulation results show that compared with the state-of-the-art protocols, our protocol can increase the delivery rate by approximately 40% at best and can reduce the delay by approximately 80%.

The integration density of electronic systems is limited by the reliability of the integrated circuits. To guarantee the overall performance, the integrated circuit reliability must be modeled and analyzed at the early design stage. This paper reviews some of the most important intrinsic aging mechanisms of MOSFETs and elaborates the physical mechanism of the coupling between aging effects. Then the progress in reliability modeling under static and dynamic operational voltages is reviewed. It is found that although these models can accurately predict the degradation in short term, they are with large errors for the long-term degradation prediction. Besides, for the circuit-level reliability modeling and simulation approach, there are still problems to be solved. This article aims to provide guidance for researchers and practitioners in integrated circuit field, and highlight the challenges for reliability research. It is of great significance to the optimization of the reliability of integrated circuits.

In practical electronic setups, most circuits are operating under dynamic condition, thus the aging models derived under static bias could lead to unexpected deviation from the real circumstance and even errors. Here in this paper, we studied the device degradation under long-period dynamic stress, which aims to mimic the device degradation at alternating voltages. Combined with the degradation characterization, models considering partial recovery after stress removal and degradation at different stress levels are developed. By comparing the test data with the model calculation, it can be seen that the calculation error of the model is within 3%, which meets the requirement of practical engineering. The proposed models could benefit the reliability design against dynamic hot carrier degradation for modern circuits.

In order to predict the circuit liftetime accurately in the design phase, precise device aging models are enssential. Although some aging simulation models have been established in academia and industry, most of the aging models consider only single influence factor, resulting in differences between simulation and test. This paper proposes an aging simulation model that considers both process variation (PV) and aging variation (AV) among different MOSFETs. By statistically analyzing the influence of aging effect on device parameters, and borrowing from the concept process corner (PC), which is used to characterize the variation in the fabrication process, here in this paper we propose to use aging corner (AC), to characterize the variation in parameter degradation among devices. The simulation results show that the new model can predict the device degradation range more accurately.

Negative bias temperature instability (NBTI) is a key factor limiting the reliability of advanced semiconductor devices. However, accurate aging modeling faces the dual challenges of complex parameter extraction in traditional physical models and the heavy data dependency of pure data-driven models. To address these issues, this article proposes a neural-network framework based on physics-related parameter decoupling and an attention mechanism, named the attention-based physics-related parameter decoupling network (APPD-Net). By structurally embedding the power-law model and utilizing multitask learning (MTL) together with a self-attention mechanism, this framework constructs a high-accuracy prediction model for NBTI degradation. Validated on experimental data from $0.18~\mu $ m pMOSFETs, APPD-Net outperforms classic neural network models such as the artificial neural network (ANN), convolutional neural network (CNN), and Transformer, even when using as little as 20% of the available training samples. Furthermore, the framework autonomously decouples the intermediate physics-related parameters $A$ and $n$ , which can be used for compact aging modeling, while maintaining strong long-term extrapolation capability. APPD-Net combines the predictive power of deep learning with the structural advantages of physical models, providing a practical approach for developing high-fidelity and explainable aging models for SPICE-oriented circuit simulation.