Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE
imec（比利时微电子研究中心）；University of Bologna（博洛尼亚大学）；Paul Scherrer Institut（保罗·谢尔研究所）
Sara Sacchi, Bappaditya Dey, Iacopo Mochi, Sandip Halder, Philippe Leray
高数值孔径极紫外光刻（High NA EUVL）的技术进步开启了对更薄光刻胶（低于30nm）的广泛研究，这是实现High NA EUVL工业化应用的必要条件。因此，扫描电子显微镜（SEM）图像面临成像对比度降低和低信噪比（SNR）的问题，影响了无偏线边缘粗糙度（uLER）和线宽粗糙度（uLWR）的测量。本工作旨在利用深度学习降噪器提升SEM图像的SNR，实现薄光刻胶的稳健粗糙度提取。本研究采集了线-空间（L/S）图案的SEM图像，采用不同厚度（15nm、20nm、25nm、30nm）、底层（旋涂玻璃-SOG、有机底层-OUL）和平均帧数（4、8、16、32和64 Fr）的化学放大型光刻胶（CAR）。降噪后，使用开源量测软件SMILE 2.3.2对含噪和降噪图像进行系统分析，研究平均CD、SNR改善因子、有偏和无偏LWR/LER功率谱密度（PSD）。帧数较少的降噪图像保持了未改变的关键尺寸（CD），提升了SNR（尤其在低积分帧数时），并能精确测量uLER和uLWR，其精度与具有持续更高帧数的含噪图像相同。因此，积分帧数少且SNR < 2的图像可被成功降噪，在保持薄光刻胶可靠粗糙度测量的同时，有利于提高量测吞吐量。
英文摘要：
The technological advance of High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL) has opened the gates to extensive researches on thinner photoresists (below 30nm), necessary for the industrial implementation of High NA EUVL. Consequently, images from Scanning Electron Microscopy (SEM) suffer from reduced imaging contrast and low Signal-to-Noise Ratio (SNR), impacting the measurement of unbiased Line Edge Roughness (uLER) and Line Width Roughness (uLWR). Thus, the aim of this work is to enhance the SNR of SEM images by using a Deep Learning denoiser and enable robust roughness extraction of the thin resist. For this study, we acquired SEM images of Line-Space (L/S) patterns with a Chemically Amplified Resist (CAR) with different thicknesses (15nm, 20nm, 25nm, 30nm), underlayers (Spin-On-Glass-SOG, Organic Underlayer-OUL) and frames of averaging (4, 8, 16, 32, and 64 Fr). After denoising, a systematic analysis has been carried out on both noisy and denoised images using an open-source metrology software, SMILE 2.3.2, for investigating mean CD, SNR improvement factor, biased and unbiased LWR/LER Power Spectral Density (PSD). Denoised images with lower number of frames present unaltered Critical Dimensions (CDs), enhanced SNR (especially for low number of integration frames), and accurate measurements of uLER and uLWR, with the same accuracy as for noisy images with a consistent higher number of frames. Therefore, images with a small number of integration frames and with SNR < 2 can be successfully denoised, and advantageously used in improving metrology throughput while maintaining reliable roughness measurements for the thin resist.
Institute for Infocomm Research（资讯通信研究院）；National University of Singapore（新加坡国立大学）；Université Pierre et Marie Curie（皮埃尔和玛丽·居里大学）
Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG feature…
Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；University of Toronto（多伦多大学）
Because noise can interfere with downstream analysis, image denoising has come to occupy an important place in the image processing toolbox. The most accurate state-of-the-art denoisers typically train on a representative dataset. But gathering a training set is not always feasible, so interest has grown in blind zero-shot denoisers that train only on the image they are denoising. The most accurate blind-zero shot methods are blind-spot networks, which mask pixels and attempt to infer them from their surroundings. Other methods exist where all neurons participate in forward inference, however …
Computer Vision Lab ETH Zürich（苏黎世联邦理工学院计算机视觉实验室）；KU Leuven（鲁汶大学）
Attribute image manipulation has been a very active topic since the introduction of Generative Adversarial Networks (GANs). Exploring the disentangled attribute space within a transformation is a very challenging task due to the multiple and mutually-inclusive nature of the facial images, where different labels (eyeglasses, hats, hair, identity, etc.) can co-exist at the same time. Several works address this issue either by exploiting the modality of each domain/attribute using a conditional random vector noise, or extracting the modality from an exemplary image. However, existing methods cann…
Tsinghua University（清华大学）；Tencent Media Lab（腾讯媒体实验室）；Research Institute of Tsinghua University in Shenzhen（清华大学深圳研究院）
Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises, giving way to methods that can adapt to existing noise without knowing its ground truth. Previous image-based method leads to noise overfitting if directly applied to video denoisers, and has inadequate temporal information management especially in terms of occlusion and lighting variation, which considerably hinders its denoising performance. In this paper, we propose a general framework for video denoising networks that successfully addresses these challenges. A novel twin sampler assembles trainin…
Hong Kong Baptist University（香港浸会大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University（深圳大学）；Instituto de Telecomunicações（电信研究院）；Instituto Superior Técnico（里斯本高等技术学院）；Universidade de Lisboa（里斯本大学）
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications…
Texas A&M University（得克萨斯农工大学）；Nanyang Technological University（南洋理工大学）；The University of Sydney（悉尼大学）
Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware details, making them suitable for various high-level computer vision tasks as the pre-processing step. However, such approaches require extra annotations for their high-level vision tasks, in order to train the joint pipeline using hybrid losses. The availability of those annotations is yet often limited to a few image sets, potentially restricting the general applicability of these methods to denoising more unseen …
The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular. At the same time, the images captured by these devices can be categorized into a small set of semantic classes. However simple, this observation has not been exploited in image denoising until now. In this paper, we demonstrate how the reconstruction quality improves when a denoiser is aware of the type of content in the image. To this end, we first propose a new fully convolutional deep neural network architecture which is sim…
MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation
SIGS, Tsinghua University（清华大学 SIGS）；Southern University of Science and Technology（南方科技大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
Cornell University（康奈尔大学）；George Washington University（乔治华盛顿大学）
We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation. As the name suggests, it relies only on simple linear interpolation of deep convolutional features from pre-trained convnets. We show that despite its simplicity, DFI can perform high-level semantic transformations like "make older/younger", "make bespectacled", "add smile", among others, surprisingly well - sometimes even matching or outperforming the state-of-the-art. This is particularly unexpected as DFI requires no specialized network architecture or even any deep ne…
University of Florida（佛罗里达大学）；Herbert Wertheim College of Engineering（赫伯特·韦特海姆工程学院）；McKnight Brain Institute（麦克奈特脑研究所）；College of Public Health and Health Professions（公共卫生与健康职业学院）；College of Medicine（医学院）；United States Air Force Research Laboratory（美国空军研究实验室）
Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. Medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. This is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages …
Image denoising and high-level vision tasks are usually handled independently in the conventional practice of computer vision, and their connection is fragile. In this paper, we cope with the two jointly and explore the mutual influence between them with the focus on two questions, namely (1) how image denoising can help improving high-level vision tasks, and (2) how the semantic information from high-level vision tasks can be used to guide image denoising. First for image denoising we propose a convolutional neural network in which convolutions are conducted in various spatial resolutions via…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Facebook Inc.（脸书公司）；Texas A&M University（得克萨斯农工大学）
Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the ge…
Arizona State University（亚利桑那州立大学）；University of Arizona（亚利桑那大学）
The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image. On a standard dataset of images we show significant improvements in reconstruction results (both in terms…
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（脑启发智能科学与技术研究院，复旦大学）；Department of Biomedical Engineering, Yale University（生物医学工程系，耶鲁大学）；Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute（生物医学成像中心，生物技术与跨学科研究所在生物医学工程系，罗切斯特理工学院）；Institute of Science and Technology for Brain-inspired Intelligence, MOE Frontiers Center for Brain Science and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Ministry of Education), Fudan University（脑启发智能科学与技术研究院，教育部脑科学前沿中心和计算神经科学与脑启发智能重点实验室，复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与脑启发技术中心）
