We present OpenRoboCare, a multimodal dataset for robot caregiving, capturing expert occupational therapist demonstrations of Activities of Daily Living (ADLs). Caregiving tasks involve complex physical human-robot interactions, requiring precise perception under occlusions, safe physical contact, and long-horizon planning. While recent advances in robot learning from demonstrations have shown promise, there is a lack of a large-scale, diverse, and expert-driven dataset that captures real-world caregiving routines. To address this gap, we collect data from 21 occupational therapists performing 15 ADL tasks on two manikins. The dataset spans five modalities—RGB-D video, pose tracking, eye-gaze tracking, task and action annotations, and tactile sensing, providing rich multimodal insights into caregiver movement, attention, force application, and task execution strategies. We further analyze expert caregiving principles and strategies, offering insights to improve robot efficiency and task feasibility. Additionally, our evaluations demonstrate that OpenRoboCare presents challenges for state-of-the-art robot perception and human activity recognition methods, both critical for developing safe and adaptive assistive robots, highlighting the value of our contribution. See our website for additional visualizations: https://emprise.cs.cornell.edu/robo-care/.

Through Silicon Vias (TSVs) are vulnerable to electromigration (EM) degradation due to their high local current densities, thereby reducing the reliability of 3D ICs with stack dies and TSVs. Due to the broad application of 3D ICs, it is necessary to analyze the electromigration reliability of TSVs. To overcome the weakness of traditional method for EM modeling of TSVs, we propose a physics-informed learning approach for transient analysis of electromigration modeling in TSV by solving the conventional mass balance equation. The proposed method allows simultaneous consideration of atomic depletion and accumulation, effective resistance degradation, electric current evolution, and stress distribution. In particular, we propose a customized neural network to simulate the EM process in TSV without the need for fine grid meshing and temporal iteration in traditional methods. Considering that the loss function of the proposed model is a combination of different loss terms, we propose a modified self-adaptive loss balanced method to automatically adjust the weights of multiple loss terms to enhance network performance. Given the prediction uncertainty due to data randomness or model architecture constraints, Gaussian probabilistic model is constructed to define the self-adaptive weights and update the dynamic weights per epoch built on maximum likelihood estimation. Compared with the finite element method, the proposed physics informed neural network method can lead to a speedup with less than 0.1% mean square error. Experimental results also show that the proposed model achieves excellent performance over other competing methods and high robustness under values of initial weights, different numbers of hidden layers and neurons per layer.

Time-dependent dielectric breakdown (TDDB) is one of the important failure mechanisms for copper (Cu) interconnects. Many TDDB models have been proposed based on different physics kinetics in the past. Recently, a physics-based TDDB model, which is based on the breakdown concept of electric path generation (EPG), has been proposed and has shown advantage over widely accepted existing electrostatic field-based TDDB assessment. However, the determination of the time-to-failure from this EPG based TDDB model includes solving partial differential equation (PDE) with time-consuming finite-element method (FEM). In recent years, deep neural networks have been proposed to predict numerical solutions of PDEs. In this paper, we use physics-informed neural network to solve the diffusion equation of ions in an electric field extracted from EPG based TDDB model. The continuous definite condition and hard constrain optimization methods are used for improving the performance of PINN in terms of accuracy and speed. Compared with the FEM method, the proposed PINN method can lead to about 100 times speedup with less than 0.1% mean squared error.

Due to the high local current densities, Through Silicon Vias (TSVs) are susceptible to electromigration (EM) degradation, which reduces the reliability of integrated circuits. Unlike traditional methods for TSV modeling and simulation, this paper introduces a unified hard constraint physics-informed learning neural network approach, called HCPINN, for the transient analysis of electromigration in TSVs by solving the conventional mass balance equation. The proposed method allows simultaneous consideration of atomic depletion and accumulation, effective resistance degradation, electric current evolution, and stress distribution. Specifically, we propose a hard constraint method for solving partial differential equations (PDEs) with general boundary conditions (BCs) for transient TSV electromigration analysis. By using the extra fields derived from the mixed finite element method, we reconstruct the corresponding PDEs by transforming general BCs into linear forms. Based on this derivation, we embed general BCs of mass balance equation into the proposed ansatz and employ sub-networks for the approximation on general BCs. The main neural network is responsible for training the internal part of the problem domain without adding loss terms with BCs, overcoming the convergence issue due to unbalanced gradients among different loss terms. Besides, we theoretically demonstrate that this reformulation of general BCs can stabilize the training process. Experimental results indicate that the proposed HCPINN exhibits superior performance and reduces boundary error in TSV electromigration analysis. Compared to the finite element method, the proposed network achieves approximately 100 times faster inference with a minimal mean squared error increase of less than 0.1%.