Microelectronics Reliability, Journal Year: 2024, Volume and Issue: 162, P. 115518 - 115518
Published: Oct. 14, 2024
Language: Английский
Microelectronics Reliability, Journal Year: 2024, Volume and Issue: 162, P. 115518 - 115518
Published: Oct. 14, 2024
Language: Английский
Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 184, P. 108583 - 108583
Published: Sept. 13, 2024
Language: Английский
Citations
3Optics and Lasers in Engineering, Journal Year: 2025, Volume and Issue: 191, P. 109010 - 109010
Published: April 14, 2025
Language: Английский
Citations
0Optics Continuum, Journal Year: 2023, Volume and Issue: 2(11), P. 2421 - 2421
Published: Nov. 7, 2023
Precision measurement of defects from optical fringe patterns is a problem significant practical relevance in non-destructive metrology. In this paper, we propose robust deep learning approach based on atrous convolution neural network model for defect detection noisy obtained diffraction phase microscopy. The utilizes the wrapped pattern as an input and generates binary image depicting non-defect regions output. effectiveness proposed validated through numerical simulations various under different noise levels. Furthermore, application technique identifying microscopy experiments also confirmed.
Language: Английский
Citations
4Applied Sciences, Journal Year: 2023, Volume and Issue: 13(21), P. 12082 - 12082
Published: Nov. 6, 2023
Convolutional neural networks (CNN) are widely used for structural damage identification. However, the presence of environmental disturbances introduces noise into acquired acceleration response data, impairing performance CNN models. In this study, we apply empirical mode decomposition (EMD) and variational (VMD) to denoise data from a steel truss bridge. By comparing smoothness convergence obtained modal functions (IMFs) using EMD VMD, confirm effectiveness VMD in smoothing denoising bridge structure signals. Additionally, propose convolutional self-attention network (CSANN) model extract features identify denoised VMD. Comparative analysis CNN, LSTM, GRU models reveals that VMD-CSANN outperforms others terms localization identification accuracy. It also exhibits excellent when handling noise-contaminated with level 10%. These findings demonstrate efficacy proposed method identifying internal structures, while maintaining robustness during processing.
Language: Английский
Citations
3Journal of Manufacturing Science and Engineering, Journal Year: 2024, Volume and Issue: 146(7)
Published: May 31, 2024
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 aims to identify various defect modes at edges, including particles, chipping, scratches, thin-film deposition, hybrid cases. diffraction patterns formed behind edge are influenced by factors, geometry, topography, presence defects. In this study, were obtained from two approaches: (1) single photodiode collected interferometric scanning (2) an imaging device coupled with objective lens captured image. first approach allowed apex characterization, while second enabled simultaneous localization characterization along bevels directions. analyzed both statistical feature extraction wavelet transform; corresponding features also evaluated through logarithm approximation. sum, methods can effectively characterize modes. Their potential lies in their applicability online metrology applications, thereby contributing advancement manufacturing processes.
Language: Английский
Citations
0Published: Jan. 1, 2024
This article introduces a robust phase derivative estimation method using deep learning-assisted subspace analysis. Simulation results validate the performance of proposed approach under severe noise conditions.
Language: Английский
Citations
0Published: Jan. 1, 2024
We present an approach that utilizes a deep learning network to compute phase gradient for defect identification. The efficacy of this method is showcased through the analysis experimentally acquired noisy interferograms.
Language: Английский
Citations
0Published: Jan. 1, 2024
The article introduces a defect identification method using digital holographic microscopy and deep learning. It utilizes wrapped phase from holograms to generate binary maps trained for high noise levels. Experimental results validate its efficacy.
Language: Английский
Citations
0Microelectronics Reliability, Journal Year: 2024, Volume and Issue: 162, P. 115518 - 115518
Published: Oct. 14, 2024
Language: Английский
Citations
0