ProgramMaster Logo
Conference Tools for MS&T24: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Deep Learning Accelerated Lab-Scale X-Ray Computed Tomography of Low-Melting-Point Solder Alloys Used in Heterogeneously Integrated Semiconductor Packages
Author(s) Eshan Ganju, Nikhilesh Chawla
On-Site Speaker (Planned) Eshan Ganju
Abstract Scope In recent years, laboratory-scale X-ray microscopy systems have increased in popularity. However, the limitations in flux within these lab-scale systems frequently result in time-consuming data acquisition, hindering the pace of scientific discovery. Deep learning (DL) methods show immense promise in enhancing low-dose lab-scale X-ray tomography data and semantically segmenting the data to extract microstructural information rapidly. This study presents an in-depth analysis of the deep learning approach to expedite the acquisition of time-resolved (4D) lab-scale absorption contrast tomography (ACT) data of low temperature solder alloys (SnBiIn) used in heterogeneously integrated semiconductor packages at different levels of aging. We used low and high-dose ACT datasets to train Generative Adversarial Networks (GANs) based models to refine the low-dose data to approach the quality of the high-dose datasets and segment the different phases within the dataset. We also present a quantitative comparison of the image quality and time savings achieved using DL-based approach.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancing AI-Driven Analysis of Synchrotron Data via FAIR Practices, Ontology and Knowledge Graphs
Advancing Sustainable Agriculture through Multiscale Spatiotemporal Data Integration and High-Performance Computing
Aligning Grains in Time-Series Laboratory Diffraction Contrast Tomography (LabDCT) Data for Machine Learning of Microstructure Evolution
Autonomous approaches for determining structure-processing-property relationships in materials
Categorization of Fracture Surfaces using Deep Learning-enabled 2D Image Analysis
Deep Learning Accelerated Lab-Scale X-Ray Computed Tomography of Low-Melting-Point Solder Alloys Used in Heterogeneously Integrated Semiconductor Packages
Enhancing Rietveld Refinement Analyses with Machine Learning Techniques
Extraction of Local Scalar 3D Microstructural Properties of SOFC Electrodes from 2D Micrographs using Convolutional Neural Networks.
Feature Extraction from SEM Images of Fatigue Fracture Surfaces
Foundation models for multimodal data mining with applications in materials science.
Hierarchical Bayesian Models for Automating Structural Materials Characterization
Machine Learning Enhanced Data Analytics for Transmission Electron Microscopy
Synthetic 3D Microstructure Generation of Solid Oxide Cell Electrodes using Denoising Diffusion Models

Questions about ProgramMaster? Contact programming@programmaster.org