In situ Fault Diagnosis of Indium Tin Oxide Electrodes by Processing S-Parameter Patterns

In the field of optoelectronics, indium tin oxide (ITO) electrodes play a crucial role in various applications, such as displays, sensors, and solar cells. Effective fault detection and diagnosis of the ITO electrodes are essential to ensure the performance and reliability of the devices. However, traditional visual inspection is challenging with transparent ITO electrodes, and existing fault detection methods have limitations in determining the root causes of the defects, often requiring destructive evaluations. In this study, an in situ fault diagnosis method is proposed using scattering parameter (S-parameter) signal processing, offering early detection, high diagnostic accuracy, noise robustness, and root cause analysis. A comprehensive S-parameter pattern database is obtained according to defect states. Deep learning (DL) approaches, including multilayer perceptron (MLP), convolutional neural network (CNN), and transformer, are then used to simultaneously analyze the cause and severity of defects. Notably, it is demonstrated that the diagnostic performance under additive noise levels can be significantly enhanced by combining different channels of the S-parameters as input to the learning algorithms, as confirmed through the t-distributed stochastic neighbor embedding (t-SNE) dimension reduction visualization.

SREL: Severity Rating Ensemble Learning for Non-Destructive Fault Diagnosis of Cu Interconnects using S-parameter Patterns

As operating frequencies and clock speeds in processors have increased over the years, interconnects affect both the reliability and performance of entire electronic systems. Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. However, existing research works utilizing electrical signals as prognostic factors have limitations, such as the inability to distinguish the root cause of defects, which eventually requires additional destructive evaluation, and vulnerability to noise that results in a false alarm. Herein, we realize the non-destructive detection and diagnosis of defects in Cu interconnects, achieving early detection, high diagnostic accuracy, and noise robustness. To the best of our knowledge, this study first simultaneously analyzes the root cause and severity using electrical signal patterns. In this paper, we experimentally show that S-parameter patterns have the ability for fault diagnosis and they are effective input data for learning algorithms. Furthermore, we propose a novel severity rating ensemble learning (SREL) approach to enhance diagnostic accuracy and noise-robustness. Our method, with a maximum accuracy of 99.3%, outperforms conventional machine learning and multi-class convolutional neural networks (CNN) as additional noise levels increase.

Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networks

In this work, we focus on outdoor lighting estimation by aggregating individual noisy estimates from images, exploiting the rich image information from wide-angle cameras and/or temporal image sequences. Photographs inherently encode information about the scene's lighting in the form of shading and shadows. Recovering the lighting is an inverse rendering problem and as that ill-posed. Recent work based on deep neural networks has shown promising results for single image lighting estimation, but suffers from robustness. We tackle this problem by combining lighting estimates from several image views sampled in the angular and temporal domain of an image sequence. For this task, we introduce a transformer architecture that is trained in an end-2-end fashion without any statistical post-processing as required by previous work. Thereby, we propose a positional encoding that takes into account the camera calibration and ego-motion estimation to globally register the individual estimates when computing attention between visual words. We show that our method leads to improved lighting estimation while requiring less hyper-parameters compared to the state-of-the-art.