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Proceedings Papers

ISTFA2021, ISTFA 2021: Tutorial Presentations from the 47th International Symposium for Testing and Failure Analysis, b1-b40, October 31–November 4, 2021,
...Abstract Abstract Presentation slides from the ISTFA 2021 tutorial, “[Machine Learning Based Data and Signal Analysis Methods for the Application in Failure Analysis].” data analysis failure analysis machine learning signal analysis DOI: 10.31399/asm.cp.istfa2021tpb1 47th...
Proceedings Papers

ISTFA2016, ISTFA 2016: Conference Proceedings from the 42nd International Symposium for Testing and Failure Analysis, 580-587, November 6–10, 2016,
... and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit. counterfeit electronic components failure analysis...
Proceedings Papers

ISTFA2018, ISTFA 2018: Conference Proceedings from the 44th International Symposium for Testing and Failure Analysis, 256-265, October 28–November 1, 2018,
... in combination with the supervised machine-learning model are used to classify different features of the golden layout and SEM images from an IC under authentication, as a unique descriptor for each type of gates. These descriptors are compared with each other to detect any subtle changes on the active region...
Proceedings Papers

ISTFA2019, ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis, 35-42, November 10–14, 2019,
... machine learning techniques for feature extraction and image segmentation that allows automated classification and predictive failure analysis on scanning acoustic microscopy (SAM) data. In the first part, conspicuous signal components of the time-domain echo signals and their weighting matrices...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 20-22, October 31–November 4, 2021,
...Abstract Abstract In the NAND flash manufacturing process, thousands of internal electronic fuses (eFuse) are tuned in order to optimize performance and validity. In this paper, we propose a machine learning optimization technique that uses deep learning (DL) and genetic algorithms (GA...
Proceedings Papers

ISTFA2020, ISTFA 2020: Papers Accepted for the Planned 46th International Symposium for Testing and Failure Analysis, 172-179, November 15–19, 2020,
... a BoM from optical images of PCBs in order to keep up to date with technological advancements. This is accomplished by revising the framework to emphasize the role of machine learning and by incorporating domain knowledge of PCB design and hardware Trojans. For accurate machine learning methods...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 12-19, October 31–November 4, 2021,
...Abstract Abstract This paper evaluates several approaches for automating the identification and classification of logos on printed circuit boards (PCBs) and ICs. It assesses machine learning and computer vision techniques as well as neural network algorithms. It explains how the authors created...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 1-5, October 31–November 4, 2021,
... of FA knowledge from numerous documents more efficient. It explains how the authors generated a dataset of FA reports along with corresponding electrical signatures and physical failures in order to train different machine-learning algorithms and compare their performance. Three of the most common...
Proceedings Papers

ISTFA2019, ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis, 445-453, November 10–14, 2019,
...: functional end point, side-channel analysis, backside thinning, milling, machine learning, second order effects backside milling electron beam microscopy electronic packaging focused ion beam functional endpoint detection integrated circuits non-destructive techniques sample preparation second...
Proceedings Papers

ISTFA2018, ISTFA 2018: Conference Proceedings from the 44th International Symposium for Testing and Failure Analysis, 550-554, October 28–November 1, 2018,
... the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube...
Proceedings Papers

ISTFA2019, ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis, 256-265, November 10–14, 2019,
... for future research in this area. Index Terms —Component Detection, PCB, Authentication, Image Analysis, Machine Learning authentication bill of materials hardware assurance image analysis machine learning printed circuit board A Review on Automatic Bill of Material Generation and Visual...
Proceedings Papers

ISTFA2020, ISTFA 2020: Papers Accepted for the Planned 46th International Symposium for Testing and Failure Analysis, 84-90, November 15–19, 2020,
... of package-specific artifacts. We use machine learning to scalably classify the activity of the chip using the QDM images and demonstrate this method for a large data set containing images that are not possible to visually classify. chip packages finite element analysis integrated circuits magnetic...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 163-171, October 31–November 4, 2021,
... of the circuit under test. This paper examines the role of cloud computing in reverse engineering, explaining how it improves throughput by orders of magnitude for 2D image registration and how it facilitates high-quality image segmentation with the help of machine learning. cloud computing machine...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 179-189, October 31–November 4, 2021,
... structures so far. In this study, we fabricated real and dummy contacts in different structures and performed a systematic SEM analysis to investigate contact charging and passive voltage contrast. Machine learning based pattern recognition was also employed to examine the possibility of differentiating real...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 291-295, October 31–November 4, 2021,
...Abstract Abstract 3D X-ray tomography plays a critical role in electronic device failure analysis, but it can take several hours to overnight to get sufficient resolution in fault regions to detect and identify defects. In this paper, we propose a machine learning based reconstruction technique...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 394-402, October 31–November 4, 2021,
...Abstract Abstract This paper presents a machine learning approach that uses genetic algorithms to optimize test program timing sets based on first silicon. The method accounts for test hardware differences, discrepancies in silicon processes, and IO pin interdependency. The general theory...
Proceedings Papers

ISTFA2020, ISTFA 2020: Papers Accepted for the Planned 46th International Symposium for Testing and Failure Analysis, 188-197, November 15–19, 2020,
... to incorporate prior object knowledge, multiple modality data, and machine learning techniques. The selected algorithms draw from both analytical and iterative approaches and a residual dense network (RDN) is employed for post-processing in the analytical FDK reconstruction. The performance of each algorithm...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 388-393, October 31–November 4, 2021,
..., such techniques are more focused on improving resolution of a specific diagnosis report, rather than in general improving the diagnosis resolution of a population of diagnosis reports as would be required for yield learning flows. Another class of techniques use supervised or semi-supervised machine learning...
Proceedings Papers

ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 65-72, October 31–November 4, 2021,
...], and Attention based models [23]. A lot of hybrid methods are being developed by combining neural networks and traditional image segmentation models for applications in critical areas of Computer Vision domain such as medical image analysis and autonomous driving [10]. However, Deep Learning, like Machine...
Proceedings Papers

ISTFA2021, ISTFA 2021: Tutorial Presentations from the 47th International Symposium for Testing and Failure Analysis, m1-m34, October 31–November 4, 2021,
... Reserved PCB Assurance Image Modalities Image Processing Machine Learning Extract Bill of Analysis and Defect Material Recognition Optical Thresholding Clustering X-ray tomography Filtering Neural Network ICs Counterfeit Thermal imaging Morphology SVM Resistors Components Etc...