Search for: All records

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. 

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. 

This paper presents the line-edge-roughness (LER) characterization of the photomask patterns and the lithography-printed patterns by enhanced knife edge interferometry (EKEI) that measures the interferometric fringe patterns occurring when the light is incident on the patterned edge. The LER is defined as a geometric deviation of a feature edge from an ideal sharp edge. The Fresnel number-based computational model was developed to simulate the fringe patterns according to the LER conditions. Based on the computational model, the photomask patterns containing LER features were designed and fabricated. Also, the patterns were printed on the glass wafer by photolithography. The interferometric fringe patterns of those two groups of patterns were measured and compared with the simulation results. By using the cross-correlation method, the LER effects on the fringe patterns were characterized. The simulation and experimental results showed good agreement. It showed that the amplitude of the fringe pattern decreases as the LER increases in both cases: photomask patterns and printed wafer patterns. As a result, the EKEI and its analysis methods showed the potential to be used in photomask design and pattern metrology, and inspection for advancing semiconductor manufacturing processes. 

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. 

Abstract. The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice and plays a key role in climate variability. It isthus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here weevaluate the mixed-layer depth (MLD) in six pairs of non-eddying (1∘ grid spacing) and eddy-rich (up to 1/16∘) models from theOcean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model evaluation, we use an updated MLD dataset computed fromobservations using the OMIP protocol (a constant density threshold). In winter, low-resolution models exhibit large biases in the deep-waterformation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable inthe mode-water formation regions of the Northern Hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remainingat high resolution. In eddy-rich models, mesoscale eddies control the spatial variability in MLD in winter. Contrary to a hypothesis that thedeepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at mostlatitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level andthe spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects. 

Abstract The subtropical oceans between 35°-20°S in the Southern Hemisphere (SH) have exhibited prevailingly rapid sea-level rise (SLR) rates since the mid-20^thcentury, amplifying damages of coastal hazards and exerting increasing threats to South America, Africa, and Australia. Yet, mechanisms of the observed SLR have not been firmly established, and its representation in climate models has not been examined. By analyzing observational sea-level estimates, ocean reanalysis products, and ocean model hindcasts, we show that the steric SLR of the SH subtropical oceans between 35°-20°S is faster than the global mean rate by 18.2%±9.9% during 1958-2014. However, present climate models—the fundamental bases for future climate projections—generally fail to reproduce this feature. Further analysis suggests that the rapid SLR in the SH subtropical oceans is primarily attributable to the persistent upward trend of the Southern Annular Mode (SAM). Physically, this trend in SAM leads to the strengthening of the SH subtropical highs, with the strongest signatures observed in the southern Indian Ocean. These changes in atmospheric circulation promote regional SLR in the SH subtropics by driving upper-ocean convergence. Climate models show systematic biases in the simulated structure and trend magnitude of SAM and significantly underestimate the enhancement of subtropical highs. These biases lead to the inability of models to correctly simulate the observed subtropical SLR. This work highlights the paramount necessity of reducing model biases to provide reliable regional sea-level projections.