Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application

Anomaly detection plays a crucial role in the industrial sector, especially in ensuring the quality of integrated circuits (IC), which are critical for product reliability and performance. With increasing demands for higher quality standards, anomaly detection during the IC manufacturing process has become a significant research focus. However, the progress of IC anomaly detection is hampered by the scarcity of defective samples and the shortage of well-defined annotations. To address this challenge, this paper focuses on the research in the field of IC, especially on ceramic package substrates (CPS). We construct a systematic automated optical inspection (AOI) equipment, and based on this, collected large-scale CPS 2D images to build a novel anomaly detection dataset (CPS2D-AD), which offers copious samples with precise annotations, including category, mask, and bounding box. To the best of our knowledge, CPS2D-AD is the largest dataset in the field of IC. Meanwhile, we conduct an extensive benchmark of CPS2D-AD, intending to supplement existing research by providing a baseline for the detection and localization of anomalies in high-resolution data of ceramic package substrates. In addition, we have developed a novel large vision model, \textbf{S}egment \textbf{A}ny \textbf{I}ntegrated \textbf{C}ircuits (SAIC), by embedding-based distillation mechanism based on CPS2D-AD datasets. Our CPS2D-AD is the first open-source anomaly detection dataset about ceramic package substrates, which can be accessed at https://anonymous.4open.science/r/CPS2D-AD