A chip inspection system based on a multiscale subarea attention network

Cavities in a weld seriously affect the airtightness of the chip, which makes chip inspection a crucial step in intelligent manufacturing. In recent years, deep learning-based defect inspection models have shown significant advantages in reducing human errors. However, due to the scarcity of defective data, deep learning-based models are susceptible to overfitting. Moreover, the multiscale and uneven grayscale distribution of cavities further compound the challenges faced by these models. To address these issues, we develop a chip inspection system based on a multiscale subarea attention network (MSANet) for cavity defect detection. In the system, the segment anything model is embedded to interactively segment the weld. Furthermore, to circumvent the overfitting problem, a large-scale cavity dataset is built by splitting the segmented weld into multiple patches. Notably, a novel MSANet is proposed to precisely segment the varying cavities, and a source-to-destination Dijkstra algorithm is designed to assess the chip quality. The experimental results demonstrate that our chip inspection system achieves a 99.24% F1-score and 99.26% AUC.