Continual shrinking of pattern dimensions in the semiconductor domain is
making it increasingly difficult to inspect defects due to factors such as the
presence of stochastic noise and the dynamic behavior of defect patterns and
types. Conventional rule-based methods and non-parametric supervised machine
learning algorithms like KNN mostly fail at the requirements of semiconductor
defect inspection at these advanced nodes. Deep Learning (DL)-based methods
have gained popularity in the semiconductor defect inspection domain because
they have been proven robust towards these challenging scenarios. In this
research work, we have presented an automated DL-based approach for efficient
localization and classification of defects in SEM images. We have proposed
SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of
semiconductor wafer defects. The use of the proposed CN approach allows
improved computational efficiency compared to previously studied DL models.
SEMI-CN gets trained to output the center, class, size, and offset of a defect
instance. This is different from the approach of most object detection models
that use anchors for bounding box prediction. Previous methods predict
redundant bounding boxes, most of which are discarded in postprocessing. CN
mitigates this by only predicting boxes for likely defect center points. We
train SEMI-CN on two datasets and benchmark two ResNet backbones for the
framework. Initially, ResNet models pretrained on the COCO dataset undergo
training using two datasets separately. Primarily, SEMI-CN shows significant
improvement in inference time against previous research works. Finally,
transfer learning (using weights of custom SEM dataset) is applied from ADI
dataset to AEI dataset and vice-versa, which reduces the required training time
for both backbones to reach the best mAP against conventional training method.

本研究提出了基于深度学习的半导体缺陷检测方法 SEMI-CenterNet，在 SEM 图像中实现了缺陷的高效定位和分类，并显著改善了推断时间。通过在两个数据集上训练和使用自定义 SEM 数据集的迁移学习，减少了传统训练方法所需的训练时间，获得了最佳的平均精确度。