IC-ChipNet: Deep Embedding Learning for Fine-grained Retrieval, Recognition, and Verification of Microelectronic Images

摘要

Modern electronic devices consist of a wide range of integrated circuits (ICs) from various manufacturers. Ensuring that an electronic device functions correctly requires verifying that its ICs and other component parts are correct and legitimate. Towards this goal, we investigate using machine learning and computer vision to identify and verify integrated circuit packages using visual features alone. We propose a deep metric learning approach to learn a feature embedding to capture important visual features of the external packages of ICs. We explore several variations of Siamese networks for this task, and learn an embedding using a joint loss function. To evaluate our approach, we collected and manually annotated a large dataset of 6,387 IC images, and tested our embedding on three challenging tasks: (1) fine-grained retrieval, (2) fine-grained IC recognition, and (3) verification. We believe this to be among the first papers targeting the novel application of fine-grained IC visual recognition and retrieval, and hope it establishes baselines to advance research in this area.