Nowadays, warpage effect is becoming one of the main concerns in the manufacture of 2.5-D/3-D IC packages. Numerical simulation of warpage in the design stage by the finite element method (FEM) is required for manufacturability and reliability optimization. 2.5-D/3-D IC packages are generally composed of laminated thin plates with high aspect ratios and complex in-plane material boundaries, leading to intrinsic difficulties in obtaining high-quality hexahedral meshes essential for fast convergence and high-quality results. In this paper, we propose a novel meshing algorithm for efficient generation of sweep hexahedral meshes towards complex 2.5-D/3-D structures. On the basis of the sweep mesh, we utilize a modified 2-D layerwise plate theory to further improve the convergence of the solver. Compared with Ansys Workbench, our meshing algorithm can either reduce the meshing time (74.7× to 221×) and the number of mesh nodes (5.26× to 18.4×), or improve the mesh quality (3.45× to 9.75×) and reduce convergence time of the solver (1.48× to 4.50×), with < 0.5% errors. A 3.75× to 12.6× reduction in convergence time is further achieved with the proposed 2-D layerwise plate theory compared to the 3-D formulation, while maintaining the errors within 3%.

Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living. Meanwhile, vehicle crowdsensing (VCS) has emerged as a key enabler, leveraging vehicles' mobility and sensor-equipped capabilities. In particular, ride-hailing vehicles can effectively facilitate flexible data collection and contribute towards urban intelligence, despite resource limitations. Therefore, this work explores a promising scenario, where edge-assisted vehicles perform joint tasks of order serving and the emerging foundation model finetuning using various urban data. However, integrating the VCS AI task with the conventional order serving task is challenging, due to their inconsistent spatio-temporal characteristics: (i) The distributions of ride orders and data point-of-interests (PoIs) may not coincide in geography, both following a priori unknown patterns; (ii) they have distinct forms of temporal effects, i.e., prolonged waiting makes orders become instantly invalid while data with increased staleness gradually reduces its utility for model fine-tuning. To overcome these obstacles, we propose an online framework based on multi-agent reinforcement learning (MARL) with careful augmentation. A new quality-of-service (QoS) metric is designed to characterize and balance the utility of the two joint tasks, under the effects of varying data volumes and staleness. We also integrate graph neural networks (GNNs) with MARL to enhance state representations, capturing graph-structured, time-varying dependencies among vehicles and across locations. Extensive experiments on our testbed simulator, utilizing various real-world foundation model fine-tuning tasks and the New York City Taxi ride order dataset, demonstrate the advantage of our proposed method.

Thermomechanical stress induced by through-silicon vias (TSVs) plays an important role in the performance and reliability analysis of 2.5D/3D ICs. While the finite element method (FEM) adopted by commercial software can provide accurate simulation results, it is very time-and memory-consuming for large-scale analysis. Over the past decade, the linear superposition method has been utilized to perform fast thermal stress estimations of TSV arrays, but it suffers from a lack of accuracy. In this paper, we propose MORE-Stress, a novel strict numerical algorithm for efficient thermal stress simulation of TSV arrays based on model order reduction. Experimental results demonstrate that our algorithm can realize a 153–504 x reduction in computational time and a 39-115x reduction in memory usage compared with the commercial software ANSYS, with negligible errors less than 1%. Our algorithm is as efficient as the linear superposition method, with an order of magnitude smaller errors and fast convergence.

With the rise of 3-D integration technology, the back-end-of-line (BEOL) interconnects start to play an important role in thermal analysis, as they inevitably occupy the main thermal dissipation path of the active devices in 3-D ICs. High-resolution full-chip thermal resistance extraction of BEOL interconnects is thus needed to obtain accurate temperatures of local hotspots, which renders rigorous numerical simulation based extraction methods unaffordable. Several analytical models have been proposed for efficient full-chip thermal resistance extraction of BEOL interconnects, but they are very inaccurate due to the inability to consider the detailed via connectivity. In this paper, we propose a novel analytical model based on the resistor network theory and the Woodbury formula. Our model takes the detailed via connectivity into consideration and achieves a 3.4× improvement in accuracy compared with the previous work, with negligible time overhead. Owing to the accuracy improvement in the extracted thermal resistances, we reduce the absolute percentage error of the maximum temperature predicted by further thermal analysis of a 3-D IC based on the extracted thermal resistances from 5.2% to 1.8%, compared with the previous work.

Given the high mobility and sensor-carrying capability, vehicle crowdsensing (VCS) has become a significant part of urban crowdsensing tasks in the development of smart cities. Ride-hailing vehicles, which are widely distributed in cities, can be a powerful tool for carrying out VCS. However, dispatching the vehicles to jointly benefit VCS and order serving is challenging, as the goals of these two tasks may not be consistent or even conflict. The distribution of ride orders and the distribution of point-of-interests (PoIs) may not coincide in time and geography. In addition, these orders and data PoIs have distinct forms of timeliness: prolonged waiting makes orders invalid and data with a larger age-of-information (AoI) has lower utility. We propose an online framework by extending multi-agent reinforcement learning (MARL) with careful augmentation to optimize the profit of order-serving and the data utility of crowdsensing. A new quality-of-service (QoS) metric is designed to characterize the utility of the two joint tasks, and formal mathematical modeling drives our MARL design. In particular, we integrated graph neural networks (GNN) to enhance state representations and capture the graph-structured dependencies among vehicles. We developed a simulator and conducted extensive experiments utilizing the New York City Taxi dataset. Experimental results demonstrate the advantage of our method in QoS improvement.

With the increasing penetration of renewable energy and electric vehicles (EVs), the behavior of EVs' charging and discharging has shown great impact on the Micro Grid power load, motivating the development of Vehicle-to-Grid (V2G) technologies. However, the V2G market is still in its infancy, due to insufficient understanding of EV users' willingness and concerns. While many studies consider direct EV control, it's more realistic to indirectly affect users' behavior through monetary incentives. For better implementation flexibility, we advocate to display at charging piles strategically chosen incentives that are combined with electricity prices. Technically, this is the first model-free learning algorithm that can optimize incentives under unknown EV user reactions, increase the load control effectiveness and users' quality-of-service (QoS) simultaneously under a long-term incentive budget, and provide theoretical performance guarantees. We first construct a bi-level optimization framework to model the time-dependencies across our solutions. We then integrate primal-dual theories and upper-confidence bounds into reinforcement learning to balance power control and incentive consumption. A dynamic programming based algorithm is also proposed to maximize the aggregate user QoS. Finally, we prove bounded sub-optimality of our learning algorithm through theoretical analysis and conduct trace-driven simulations to demonstrate the advantages of our bi-level framework.