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The precision metrology of through-hole silicon via (TSV) in the semiconductor industry has remained a critical challenge as its critical dimension (CD) reduces. In this letter, we report a novel method for TSV geometric feature measurement and characterization. By illuminating a collimated infrared laser beam to the TSV and then analyzing the TSV edge-induced diffraction interferometric fringe patterns, multiple geometric information of the TSV could be characterized, establishing its database. This computational approach to TSV characterization was validated by experiments. Being non-destructive and easy to deploy, this method provides a low cost and high efficiency solution for TSV metrology. 

Abstract Tropical cyclones (TCs) are often generated from preexisting “seed” vortices. Seeds with higher persistence might have a higher chance to undergo TC genesis. What controls seed persistence remains unclear. This study proposes that planetary Rossby wave drag is a key factor that affects seed persistence. Using recently developed theory for the response of a vortex to the planetary vorticity gradient, a new parameter given by the ratio of the maximum wind speed (V_max) to the Rhines speed at the radius of maximum wind (R_max), here termed “vortex structural compactness” (C_υ), is introduced to characterize the vortex weakening by planetary Rossby wave drag. The relationship between vortex compactness and weakening rate is tested via barotropicβ-plane experiments. The vortex’s initialC_υis varied by systematically varying their initialV_maxandR_maxin idealized wind profile models. Experiments are also conducted with real-world seed vortices from reanalysis data, which possess natural compactness variability. The weakening rate depends strongly on the vortex’s initialC_υacross both idealized and real-world experiments, and the initial axis-asymmetry introduces minor differences. Experiments doubling the size of seed vortices cause them to weaken more rapidly, in line with other experiment sets. The dependence of the weakening rate on initial compactness can be predicted from a simple theory, which is more robust for more compact vortices. Our results suggest that a seed’s structure strongly modulates how long it can persist in the presence of a planetary vorticity gradient. Connections to real seeds on Earth are discussed. Significance StatementThis study explores the evolution of tropical cyclone (TC) seeds, which are preexisting weakly rotating rainstorms, in a simple setting that isolates the dynamical effects of the rotating sphere. It is not clear why some seeds can persist for a longer duration and might have a higher chance to eventually undergo genesis. We proposed that a factor called “planetary Rossby wave drag” plays a crucial role in this process. To investigate this, we introduce a new parameter called “compactness” to describe how the size and intensity of a seed vortex determines how quickly it will weaken due to this drag. We conducted experiments with numerical simulations and real-world TC seeds to test our ideas. Our findings show that the initial compactness of seeds strongly influences how quickly they weaken. We have developed a formula to predict how quickly these seeds weaken based on their compactness, which is especially accurate for more compact seeds. This research helps us understand how planetary Rossby wave drag affects the persistence of a TC seed and, ultimately, how it might impact the frequency of TCs. 

This paper introduces a new digital integration that combines edge diffractometry with convolutional neural networks (CNN) for via metrology and inspection. The beam propagation method (BMP) was used to simulate the interferogram generated by edge diffractometry to characterize via edge roughness (VER). A comprehensive database was established to link different fringe patterns to VER for CNN training. The well-trained CNN-based methodology provided a fast and accurate assessment of VER, with a root mean squared error (RMSE) of 0.073 and an average mean absolute deviation ratio (MADR) of 2.274%. In addition, the proposed digital approach was compared to the multilayer perceptron machine (MLP) in terms of computational efficiency and predictive accuracy. The proposed digital integration greatly improved the accuracy and speed of VER measurement, characterization, and quantification, potentially enhancing device yield and reliability. The successful application of this digital approach could open up possibilities for various types of via or pattern metrology. 

Abstract This paper introduces a novel wafer-edge quality inspection method based on analysis of curved-edge diffractive fringe patterns, which occur when light is incident and diffracts around the wafer edge. The proposed method aims to identify various defect modes at the wafer edges, including particles, chipping, scratches, thin-film deposition, and hybrid defect cases. The diffraction patterns formed behind the wafer edge are influenced by various factors, including the edge geometry, topography, and the presence of defects. In this study, edge diffractive fringe patterns were obtained from two approaches: (1) a single photodiode collected curved-edge interferometric fringe patterns by scanning the wafer edge and (2) an imaging device coupled with an objective lens captured the fringe image. The first approach allowed the wafer apex characterization, while the second approach enabled simultaneous localization and characterization of wafer quality along two bevels and apex directions. The collected fringe patterns were analyzed by both statistical feature extraction and wavelet transform; corresponding features were also evaluated through logarithm approximation. In sum, both proposed wafer-edge inspection methods can effectively characterize various wafer-edge defect modes. Their potential lies in their applicability to online wafer metrology and inspection applications, thereby contributing to the advancement of wafer manufacturing processes. 

Ultrasonic welding machines play a critical role in the lithium battery industry, facilitating the bonding of batteries with conductors. Ensuring high-quality welding is vital, making tool condition monitoring systems essential for early-stage quality control. However, existing monitoring methods face challenges in cost, downtime, and adaptability. In this paper, we present WeldMon, an affordable ultrasonic welding machine condition monitoring system that utilizes a custom data acquisition system and a data analysis pipeline designed for real-time analysis. Our classification algorithm combines auto-generated features and hand-crafted features, achieving superior cross-validation accuracy (95.8% on average over all testing tasks) compared to the state-of-the-art method (92.5%) in condition classification tasks. Our data augmentation approach alleviates the dataset shift problem, enhancing tool condition classification accuracy by 8.3%. All algorithms run locally, requiring only 385 milliseconds to process data for each welding cycle. We deploy WeldMon and a commercial system on an actual ultrasonic welding machine, performing a comprehensive comparison. Our findings highlight the potential for developing cost-effective, high-performance, and reliable tool condition monitoring systems. 

This paper introduces a simple three-dimensional (3D) stereoscopic method using a single unit of an imaging device consisting of a charge-coupled device (CCD) and a zoom lens. Unlike conventional stereoscopy, which requires a pair of imaging devices, 3D surface imaging is achieved by 3D image reconstruction of two images obtained from two different camera positions by scanning. The experiments were performed by obtaining two images of the measurement target in two different ways: (1) by moving the object while the imaging device is stationary, and (2) by moving the imaging device while the object is stationary. Conventional stereoscopy is limited by disparity errors in 3D image reconstruction because a pair of imaging devices is not ideally identical and alignment errors are always present in the imaging system setup. The proposed method significantly reduced the disparity error in 3D image reconstruction, and the calibration process of the imaging system became simple and convenient. The proposed imaging system showed a disparity error of 0.26 in the camera pixel.