  With continuous progression of Moore's Law, integrated circuit (IC) device
complexity is also increasing. Scanning Electron Microscope (SEM) image based
extensive defect inspection and accurate metrology extraction are two main
challenges in advanced node (2 nm and beyond) technology. Deep learning (DL)
algorithm based computer vision approaches gained popularity in semiconductor
defect inspection over last few years. In this research work, a new
semiconductor defect inspection framework "SEMI-DiffusionInst" is investigated
and compared to previous frameworks. To the best of the authors' knowledge,
this work is the first demonstration to accurately detect and precisely segment
semiconductor defect patterns by using a diffusion model. Different feature
extractor networks as backbones and data sampling strategies are investigated
towards achieving a balanced trade-off between precision and computing
efficiency. Our proposed approach outperforms previous work on overall mAP and
performs comparatively better or as per for almost all defect classes (per
class APs). The bounding box and segmentation mAPs achieved by the proposed
SEMI-DiffusionInst model are improved by 3.83% and 2.10%, respectively. Among
individual defect types, precision on line collapse and thin bridge defects are
improved approximately 15\% on detection task for both defect types. It has
also been shown that by tuning inference hyperparameters, inference time can be
improved significantly without compromising model precision. Finally, certain
limitations and future work strategy to overcome them are discussed.
