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convolutional neural networks

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Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 418-422, October 31–November 4, 2021,
... via optical microscopy. The goal of this work is to quantify this relationship using computer vision. As explained in the paper, the authors trained a convolutional neural network to estimate the thickness of dielectric films based on images and measurements recorded during processing. The trained...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 12-19, October 31–November 4, 2021,
... and ORB Fully Connected Neural Networks (FCN), and Convolutional Neural Network (CNN) architectures. It also discusses edge cases where the algorithms are prone to fail and where potential opportunities exist for future work in PCB logo identification, component authentication, and counterfeit detection...
Proceedings Papers

ISTFA2019, ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis, 35-42, November 10–14, 2019,
... imaging. In the second part, the before mentioned signal separation was employed to generate a labeled dataset for training and finetuning of a classification model based on a one-dimensional convolutional neural network. The learning model was sensitive to critical features of the given task without...
Proceedings Papers

ISTFA2021, ISTFA 2021: Tutorial Presentations from the 47th International Symposium for Testing and Failure Analysis, b1-b40, October 31–November 4, 2021,
... of Machine Learning Features and Label Application in failure Analysis Machine Learning Models Machine Learning Workflow Artificial neural networks Neural Networks Convolutional Neural Networks Machine Learning Models Case Study I Case Study II Case Study III 2 Artificial Intelligence...
Proceedings Papers

ISTFA2019, ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis, 256-265, November 10–14, 2019,
... approaches for automatic visual inspection, however there is no framework proposed for such an effort. Malge et al., adopted canonical image processing methods to detect trace level and via level defects [7] while Tang et al., employed Convolutional Neural Network (CNN) to detect similar defects [8]; other...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 291-295, October 31–November 4, 2021,
... Reconstruction Deep learning based convolutional neural networks have shown excellent performance in numerous computer vision tasks such as recognition, segmentation, resolution improvement, and denoising [5-8]. However, the reported methods are not directly useful to X-ray microscopy, where an actual 3D image...
Proceedings Papers

ISTFA2018, ISTFA 2018: Conference Proceedings from the 44th International Symposium for Testing and Failure Analysis, 363-367, October 28–November 1, 2018,
... and generalization. We have chosen a Faster R-CNN approach which uses convolutional neural networks, for its multilayer design allowing better classification performance. The design is as follows: there are two neural network modules, the first serves as region proposal network, the second one acts as classifier...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 20-22, October 31–November 4, 2021,
... for convolutional neural network (CNN) [3]. CNN can implement 60 % shorter DL training time than MLP, which is helpful to reduce the optimization turnaround time for mass production. Figure 4(b) shows the network ensemble technique to improve the accuracy of DL and provide various output results, which is used...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 163-171, October 31–November 4, 2021,
..., generating an incorrect and valueless segmentation. To overcome the threshold limitation, we implemented machine learning (ML) based segmentation for wires and vias. For wires a Convolutional Neural Network (CNN), more specifically a Generative Adversarial Network (GAN) based, segmentation was implemented...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 65-72, October 31–November 4, 2021,
...) based image segmentation methods. These DL based segmentation approaches utilize various NN architectures including Fully Convolutional Models [18], Convolutional Neural Networks with graphical models [19], Encoder-Decoder based models [20], R-CNN based application. In the PCB assurance domain...
Proceedings Papers

ISTFA2018, ISTFA 2018: Conference Proceedings from the 44th International Symposium for Testing and Failure Analysis, 272-279, October 28–November 1, 2018,
...-samples analysis using machine learning techniques (based on Convolutional Neural Networks). This is undergoing investigation and the main concern is to be able to reach the main level of detection (while drastically reducing the recognition processing time) Statistical analysis At the layer of interest...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 96-107, October 31–November 4, 2021,
... the magnetic field image of the total structure. This allows for isolation of signal layers and can be used to map embedded current paths via solution of the 2D magnetic inverse. In addition, the paper also discusses the use of neural networks to identify 2D current distributions and its potential...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 179-189, October 31–November 4, 2021,
... be a vast improvement in performance if deep learning was applied there currently is not enough data of camouflaged contacts to effectively train an artificial neural network (ANN) or convolutional neural network (CNN) for classification between covert and genuine gates. from SEM images, the features...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 6-11, October 31–November 4, 2021,
... on Neural Networks, pp.10(3):626-634, 1999 [12] Stone, J., Independent Component Analysis: A Tutorial Introduction (2004) pp. 108-109 11 Copyright © 2021 ASM International. All rights reserved. 2021 ASM International ...