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.

翻译：随着摩尔定律的持续推进，集成电路器件复杂度不断增加。基于扫描电子显微镜图像的大范围缺陷检测与精确计量提取是先进节点（2纳米及以下）技术面临的两大挑战。近年来，基于深度学习算法的计算机视觉方法在半导体缺陷检测领域逐渐普及。本研究提出并对比了一种新型半导体缺陷检测框架"Semi-DiffusionInst"。据作者所知，本研究首次展示了利用扩散模型精确检测并精细分割半导体缺陷模式的能力。通过探索不同特征提取网络作为骨干网络以及数据采样策略，在精度与计算效率之间实现了平衡折中。所提方法在总体平均精度上优于先前工作，且在几乎所有缺陷类别上表现相当或更优。相较于基线模型，Semi-DiffusionInst在边界框和分割平均精度上分别提升了3.83%和2.10%。在单个缺陷类型中，线断缺陷与细桥缺陷的检测精度均提升约15%。研究还表明，通过调整推理超参数，可在不牺牲模型精度的前提下显著提升推理速度。最后，本文讨论了当前方法的局限性及未来改进策略。