Microelectronics defects represent a considerable hazard to various sectors, including industry,

government, and society at large, necessitating efficient detection methods to avert or lessen

potential harm. The primary challenge in identifying these defects lies in the scarcity of ground

truth data, which hampers both human experts and machine learning algorithms from

accurately identifying and categorizing new instances of defective components. To address this,

in this proposal, which is an extension of a 2023 proposal, we introduce a comprehensive data

augmentation strategy designed to fabricate virtual models of defective or counterfeit parts,

effectively bridging the data gap and facilitating the generation of extensive datasets with

minimal expense. This innovative approach not only supplies automatically labeled ground

truth data for training machine-learning algorithms in image segmentation, enhancing the

reverse engineering of components through diverse image modalities, but also proposes the

creation of digital twins for each part. These digital twins, accessible through a user-friendly

cloud-based platform, offer a revolutionary way to examine and interact with the components

virtually. Users can explore various microscopy images and spectroscopy data, simulate

different characterizations, and collaborate on annotations and analyses in real-time, regardless

of their geographic location. This feature is especially beneficial for training purposes, enabling

remote collaboration and visualization of image analyses through shared sessions without

necessitating specialized software or hardware. Our proposal aims to not only surmount the

challenges posed by the lack of ground truth data but also to revolutionize how defective parts

are identified, analyzed, and studied, making it a game-changer for quality control in

microelectronics manufacturing and beyond.