As semiconductor patterning dimensions shrink, more advanced Scanning
Electron Microscopy (SEM) image-based defect inspection techniques are needed.
Recently, many Machine Learning (ML)-based approaches have been proposed for
defect localization and have shown impressive results. These methods often rely
on feature extraction from a full SEM image and possibly a number of regions of
interest. In this study, we propose a deep Reinforcement Learning (RL)-based
approach to defect localization which iteratively extracts features from
increasingly smaller regions of the input image. We compare the results of 18
agents trained with different feature extractors. We discuss the advantages and
disadvantages of different feature extractors as well as the RL-based framework
in general for semiconductor defect localization.

该研究通过使用深度强化学习方法，从逐渐缩小的输入图像区域中迭代地提取特征，比较了使用不同特征提取器训练的 18 个代理商的结果，并讨论了不同特征提取器的优缺点以及针对半导体缺陷定位的深度强化学习框架的一般优势和劣势。

基于强化学习的半导体缺陷定位的特征提取器基准化

Benchmarking Feature Extractors for Reinforcement Learning-Based  Semiconductor Defect Localization

During industrial processing, unforeseen defects may arise in products due to
uncontrollable factors. Although unsupervised methods have been successful in
defect localization, the usual use of pre-trained models results in
low-resolution outputs, which damages visual performance. To address this
issue, we propose PyramidFlow, the first fully normalizing flow method without
pre-trained models that enables high-resolution defect localization.
Specifically, we propose a latent template-based defect contrastive
localization paradigm to reduce intra-class variance, as the pre-trained models
do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for
multi-scale fusing and volume normalization to help generalization. Our
comprehensive studies on MVTecAD demonstrate the proposed method outperforms
the comparable algorithms that do not use external priors, even achieving
state-of-the-art performance in more challenging BTAD scenarios.

提出的 PyramidFlow 方法是第一个无需预训练模型就能实现高分辨率瑕疵定位的全归一化流法，该方法利用金字塔状的归一化流进行多尺度融合和体积归一化来帮助泛化，并提出一种基于潜变量模板的瑕疵对比定位范式来降低类内方差。综合研究 MVTecAD 的研究表明，该方法表现优于不使用外部 prior 的可比算法，甚至在更具挑战性的 BTAD 场景中达到了最先进的表现。

金字塔归一化流在高分辨率缺陷对比定位中的应用

PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

In industrial manufacturing processes, errors frequently occur at
unpredictable times and in unknown manifestations. We tackle the problem of
automatic defect detection without requiring any image samples of defective
parts. Recent works model the distribution of defect-free image data, using
either strong statistical priors or overly simplified data representations. In
contrast, our approach handles fine-grained representations incorporating the
global and local image context while flexibly estimating the density. To this
end, we propose a novel fully convolutional cross-scale normalizing flow
(CS-Flow) that jointly processes multiple feature maps of different scales.
Using normalizing flows to assign meaningful likelihoods to input samples
allows for efficient defect detection on image-level. Moreover, due to the
preserved spatial arrangement the latent space of the normalizing flow is
interpretable which enables to localize defective regions in the image. Our
work sets a new state-of-the-art in image-level defect detection on the
benchmark datasets Magnetic Tile Defects and MVTec AD showing a 100% AUROC on 4
out of 15 classes.

本研究提出了一种新的全卷积跨尺度归一化流模型（CS-Flow），该模型通过对不同尺度的多个特征图进行联合处理，能够对工业制造中的缺陷进行自动检测和定位，同时在基准数据集上取得了 100％的区分度（AUROC)。