Unsupervised graph-based image clustering for pretext distribution learning in IC assurance - Tee, Yee Yang (2023)
Delayered integrated circuit (IC) image analysis is one of the most reliable approaches for the hardware assurance of ICs. Deep learning (DL) techniques have been proposed as an effective means to automate IC image analysis due to their high accuracy and efficiency, allowing for automated learning from labeled training data. These data-driven approaches typically require a large amount of comprehensive training samples that are representative of the testing dataset. Curating such suitable datasets for deep learning training is a challenging and time-consuming task, especially when working with large-scale IC image datasets. Automatically analyzing the distribution of the IC images would be an efficient method to curate ideal datasets for deep learning. In this paper, we propose an unsupervised graph-based image clustering framework for pretext distribution learning of delayered IC images. Our proposed framework consists of a pre-trained convolutional neural network (CNN) in the feature extraction stage, a graph-based feature clustering stage, and an image retrieval stage. In our experiments, we achieved a best normalized mutual information score of 0.6407 for the clustering of a benchmark dataset containing 1911 delayered IC images. Our framework has excellent ability to retrieve visually similar images from the benchmark dataset when queried with unseen image samples.