Boundary Enhanced Semantic Segmentation for High Resolution Electron Microscope Images

This work proposes an automated semantic segmentation approach for high resolution scanning electron microscope images, which enables the detection of hardware Trojans and counterfeit integrated circuits. We evaluate state of the art segmentation approaches and leverage expert domain knowledge to propose a neural network architecture tailored for our use case. We further address the challenge of the limited availability of training images and evaluate which pre-trained encoder can be leveraged most effectively for the given use case. The proposed segmentation network uses expert domain knowledge to account for the importance of separating technology features on a fine-grain level by introducing a separate boundary stream. The test results compare our network to a baseline approach and to two state-of-the-art segmentation networks.