基于SE-CPN模型的芯片表面缺陷检测

Chip Surface Defect Detection Based on SE-CPN Model

为解决芯片表面缺陷检测的困难,提出新的神经网络模型SE-CPN,引入注意力机制模块,采用自底向上路径聚合的方式检测不同尺度的特征。为便于SE-CPN模型在边缘设备上部署,采用Ghost模块,保证相似的识别性能同时大幅度降低计算量,使存储占用空间降低到16.3 MB。通过各项数据增强手段,在小样本基础上建立了缺陷芯片数据集,训练并评价模型。实验表明,在此数据集上文中模型平均准确率达到98.2%。

To solve the problem of chip surface defect detection, a new neural network SE-CPN model was proposed. Attention mechanism module was introduced to detect different scale features by bottomup path aggregation. To facilitate SE-CPN model deployment on edge devices, Ghost module was used to ensure similar recognition performance while significantly reducing the computational load and reducing the storage space to 16.3 MB. Through various data enhancement methods, a defect chip dataset was established on the basis of small samples, and a training and evaluation model was established. Experiments show that the average model accuracy over this dataset is 98.2%.