이상 탐지를 이용한 불분명한 표면을 가지는 웨이퍼 표면에서의 크랙 검출

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2023. 2. 김도년.Defect detection is a crucial process to improve the productivity and quality of products in the industry. However, defects in the nanoscale-manufacture become difficult to detect, since the shapes of the defects are complex and noises and unclean backgrounds cover the defects frequently. It is laborious and inefficient to utilize human resources for defect detection because the rate of defects in the industry is extremely low and it requires professional knowledge to detect the defects in some cases. Applying an anomaly detection model as a defect detector in the industry is the best solution which will save time and human resources. However, there are many difficulties to apply the data-driven based anomaly detection model to real industry inspection. In our research, we found that our target product wafers contain resin bleed, which hinders detecting cracks on the wafer surfaces. The resin bleed impedes the anomaly detection on wafers because it is similar to the cracks in the wafer and at the same time it belongs to the normal components. In this paper, we propose a method to improve the crack detection performance of the anomaly detection model by enhancing the edge information of cracks. Our model achieved 96.7% at the image level AUROC and 98.6% at pixel level AUROC by improving 4.5% and 2.0% respectively without additional annotation.결함 탐지는 산업에서 제품의 생산성이나 질을 향상시키는데 매우 중요한 과정입니다. 그러나 나노 스케일 공정에서 결함의 형상이나 노이즈, 불분명한 배경 같은 요소들은 결함 탐지를 어렵게 만듭니다. 산업에서 결함의 비율은 매우 작고 결함 탐지를 위해서 전문적인 지식을 필요로 하는 경우도 많기 때문에 사람이 직접 결함 탐지를 수행하는 것은 매우 소모적이고 비효율적입니다. 그러므로 산업에서 컴퓨터 비전 기반 결함 탐지 모델을 활용하는 것은 시간이나 물적, 인적 자원을 절약하고 부족한 결함 데이터 문제도 해결할 수 있는 훌륭한 방법입니다. 그러나 데이터 기반 이상 탐지 모델을 실제 산업 검사에 활용하는 것은 많은 어려움을 가지고 있습니다. 해당 연구에서 우리는 결함 탐지의 목표로 하는 웨이퍼 제품에서 resin bleed 라는 크랙 검출을 방해하는 요소를 확인했습니다. resin bleed는 정상 요소에 속하지만 머신 비전의 관점에서는 크랙과 비슷합니다. 이러한 특징들은 데이터 셋 전체에 분포되어 있는 Resin bleed가 결함 탐지 모델이 크랙들을 정상 요소들과 분명하게 구별할 수 있는 능력을 저해합니다. 이 논문에서 우리는 크랙의 엣지 성분을 강화하여 이상 탐지 모델이 크랙을 더 잘 검출할 수 있도록 하는 방법을 제시합니다. 저희가 제안하는 방법들은 결함 탐지 성능을 이미지 레벨에서 96.7%, 픽셀 레벨에서 98.6% 성능을 달성했습니다. 저희가 달성한 성과들은 기존 이상 탐지 모델을 사용했을 때와 비교하여 추가 데이터 주석 없이 이미지 레벨에서 4.5%, 픽셀 레벨에서 2.0% 성능 향상한 결과입니다.Abstract 1 Table of contents 2 List of tables, figures 3 Chapter 1. Introduction 5 1.1 Anomaly Detection 7 1.2 Wafer Defect Detection 9 1.3 Crack Detection 10 Chapter 2. Edge-Enhanced Anomaly Detection 12 2.1 Edge Information Extraction 13 2.2 Edge-Enhanced Features into a Memory Bank 17 2.3 Effective Memory Bank Subset Search 19 2.4 Algorithm for Anomaly Detection and Localization 20 Chapter 3. Model Validation on Wafer Dataset 24 3.1 Experiments Detail 3.1.1 Datasets and Training Details 24 3.1.2 Evaluation Metrics for Anomaly Detection 25 3.2 Anomaly Detection on Wafer Surface 25 3.3 Result Analysis 40 3.3 Comparison Study for Selecting Edge Features 44 Chapter 4. Conclusions 50 Bibliography 51 Abstract in Korean 55석

Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

With the increasing use of induction thermography (IT) for non-destructive testing (NDT) in the mechanical and rail industry, it becomes necessary for the manufactures to rapidly and accurately monitor the health of specimens. The most general problem for IT detection is due to strong noise interference. In order to counter it, general post-processing is carried out. However, due to the more complex nature of noise and irregular shape specimens, this task becomes difficult and challenging. In this paper, a low-rank tensor with a sparse mixture of Gaussian (MoG) (LRTSMoG) decomposition algorithm for natural crack detection is proposed. The proposed algorithm models jointly the low rank tensor and sparse pattern by using a tensor decomposition framework. In particular, the weak natural crack information can be extracted from strong noise. Low-rank tensor based iterative sparse MoG noise modeling is carried out to enhance the weak natural crack information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted for natural crack detection on a variety of specimens. A comparative analysis is presented with general tensor decomposition algorithms. The algorithms are evaluated quantitatively based on signal-to-noise-ratio (SNR) along with the visual comparative analysis

Crack Detection Of Eggshell Featuring An Improved Anisotropic Diffusion Filter And Support Vector Machine

Cracks on eggshell are categorized into two types: (i) macro-crack, and (ii) micro-crack. Unlike macro-crack, the detection of micro-crack is very difficult and challenging since this type of defect is invisible to naked eyes. This problem has been partially solved by utilizing a custom made candling light in the background illumination set-up. Even though this has improved the visibility of micro-crack pixels, however this imaging technique has also enhanced anomalies and other unwanted pixels, leading to a very cluttered and noisy images. A three-stage window-free method was proposed to solve this problem. In the first stage, line enhancement was implemented in order to enhance the quality of line in the image. Next, the crack enhancement was performed using an improved anisotropic diffusion filter. In this case, cracks are characterized by pixels having high intensity and high gradient values. Using these characteristics, the detection system has been developed to inspect eggshells and classify them into one of the following three possible classes: (i) intact, (ii) micro-crack, and (iii) macro-crack. In the third stage, a modified double thresholding was employed to further highlight crack pixels. Results indicate that the proposed method is competitive when compared with existing techniques and achieved better performance in terms of FOM. On average the method has resulted in FOM of 0.73 compared to 0.67, 0.57 and 0.42 produced by the original and two recent variants of anisotropic diffusion filter for crack enhancement, and 0.52, 0.68 and 0.48 produced by Otsu, Sobel and Canny techniques for image segmentation. Meanwhile the classifications has been performed using the state of the art twin bounded support vector machine (TBSVM) and the results have been compared with the standard support vector machine (SVM) utilizing three different approaches: (i) one-versus-all (OVA), (ii) one-versus-one (OVO), and (iii) directed acyclic graph (DAG). Results reveal that DAG outperforms OVA and OVO with sensitivity, specificity and accuracy averaging at 93.1%, 96.5% and 93.0% for TBSVM compared to 90.7%, 95.4% and 90.7% for standard SVM. Meanwhile the ROC performance indicates that this classifier can distinguish between intact and macro-crack samples with 100% certainty. The performance decreases insignificantly when distinguishing intact from micro-crack and micro-crack from macro-crack samples. Therefore, these results suggest that the proposed detection system is useful and effective for applications in egg processing

Acoustic Waves

The concept of acoustic wave is a pervasive one, which emerges in any type of medium, from solids to plasmas, at length and time scales ranging from sub-micrometric layers in microdevices to seismic waves in the Sun's interior. This book presents several aspects of the active research ongoing in this field. Theoretical efforts are leading to a deeper understanding of phenomena, also in complicated environments like the solar surface boundary. Acoustic waves are a flexible probe to investigate the properties of very different systems, from thin inorganic layers to ripening cheese to biological systems. Acoustic waves are also a tool to manipulate matter, from the delicate evaporation of biomolecules to be analysed, to the phase transitions induced by intense shock waves. And a whole class of widespread microdevices, including filters and sensors, is based on the behaviour of acoustic waves propagating in thin layers. The search for better performances is driving to new materials for these devices, and to more refined tools for their analysis

Engineering aperiodic spiral order for photonic-plasmonic device applications

Thesis (Ph.D.)--Boston UniversityDeterministic arrays of metal (i.e., Au) nanoparticles and dielectric nanopillars (i.e., Si and SiN) arranged in aperiodic spiral geometries (Vogel's spirals) are proposed as a novel platform for engineering enhanced photonic-plasmonic coupling and increased light-matter interaction over broad frequency and angular spectra for planar optical devices. Vogel's spirals lack both translational and orientational symmetry in real space, while displaying continuous circular symmetry (i.e., rotational symmetry of infinite order) in reciprocal Fourier space. The novel regime of "circular multiple light scattering" in finite-size deterministic structures will be investigated. The distinctive geometrical structure of Vogel spirals will be studied by a multifractal analysis, Fourier-Bessel decomposition, and Delaunay tessellation methods, leading to spiral structure optimization for novel localized optical states with broadband fluctuations in their photonic mode density. Experimentally, a number of designed passive and active spiral structures will be fabricated and characterized using dark-field optical spectroscopy, ellipsometry, and Fourier space imaging. Polarization-insensitive planar omnidirectional diffraction will be demonstrated and engineered over a large and controllable range of frequencies. Device applications to enhanced LEDs, novel lasers, and thin-film solar cells with enhanced absorption will be specifically targeted. Additionally, using Vogel spirals we investigate the direct (i.e. free space) generation of optical vortices, with well-defined and controllable values of orbital angular momentum, paving the way to the engineering and control of novel types of phase discontinuities (i.e., phase dislocation loops) in compact, chip-scale optical devices. Finally, we report on the design, modeling, and experimental demonstration of array-enhanced nanoantennas for polarization-controlled multispectral nanofocusing, nanoantennas for resonant near-field optical concentration of radiation to individual nanowires, and aperiodic double resonance surface enhanced Raman scattering substrates