283 research outputs found
Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review
A growing need exists for efficient and accurate methods for detecting
defects in semiconductor materials and devices. These defects can have a
detrimental impact on the efficiency of the manufacturing process, because they
cause critical failures and wafer-yield limitations. As nodes and patterns get
smaller, even high-resolution imaging techniques such as Scanning Electron
Microscopy (SEM) produce noisy images due to operating close to sensitivity
levels and due to varying physical properties of different underlayers or
resist materials. This inherent noise is one of the main challenges for defect
inspection. One promising approach is the use of machine learning algorithms,
which can be trained to accurately classify and locate defects in semiconductor
samples. Recently, convolutional neural networks have proved to be particularly
useful in this regard. This systematic review provides a comprehensive overview
of the state of automated semiconductor defect inspection on SEM images,
including the most recent innovations and developments. 38 publications were
selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of
these, the application, methodology, dataset, results, limitations and future
work were summarized. A comprehensive overview and analysis of their methods is
provided. Finally, promising avenues for future work in the field of SEM-based
defect inspection are suggested.Comment: 16 pages, 12 figures, 3 table
์ ์กฐ ์์คํ ์์์ ์์ธก ๋ชจ๋ธ๋ง์ ์ํ ์ง๋ฅ์ ๋ฐ์ดํฐ ํ๋
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ฐ์
๊ณตํ๊ณผ, 2021. 2. ์กฐ์ฑ์ค.Predictive modeling is a type of supervised learning to find the functional relationship between the input variables and the output variable. Predictive modeling is used in various aspects in manufacturing systems, such as automation of visual inspection, prediction of faulty products, and result estimation of expensive inspection. To build a high-performance predictive model, it is essential to secure high quality data. However, in manufacturing systems, it is practically impossible to acquire enough data of all kinds that are needed for the predictive modeling. There are three main difficulties in the data acquisition in manufacturing systems. First, labeled data always comes with a cost. In many problems, labeling must be done by experienced engineers, which is costly. Second, due to the inspection cost, not all inspections can be performed on all products. Because of time and monetary constraints in the manufacturing system, it is impossible to obtain all the desired inspection results. Third, changes in the manufacturing environment make data acquisition difficult. A change in the manufacturing environment causes a change in the distribution of generated data, making it impossible to obtain enough consistent data. Then, the model have to be trained with a small amount of data. In this dissertation, we overcome this difficulties in data acquisition through active learning, active feature-value acquisition, and domain adaptation. First, we propose an active learning framework to solve the high labeling cost of the wafer map pattern classification. This makes it possible to achieve higher performance with a lower labeling cost. Moreover, the cost efficiency is further improved by incorporating the cluster-level annotation into active learning. For the inspection cost for fault prediction problem, we propose a active inspection framework. By selecting products to undergo high-cost inspection with the novel uncertainty estimation method, high performance can be obtained with low inspection cost. To solve the recipe transition problem that frequently occurs in faulty wafer prediction in semiconductor manufacturing, a domain adaptation methods are used. Through sequential application of unsupervised domain adaptation and semi-supervised domain adaptation, performance degradation due to recipe transition is minimized. Through experiments on real-world data, it was demonstrated that the proposed methodologies can overcome the data acquisition problems in the manufacturing systems and improve the performance of the predictive models.์์ธก ๋ชจ๋ธ๋ง์ ์ง๋ ํ์ต์ ์ผ์ข
์ผ๋ก, ํ์ต ๋ฐ์ดํฐ๋ฅผ ํตํด ์
๋ ฅ ๋ณ์์ ์ถ๋ ฅ ๋ณ์ ๊ฐ์ ํจ์์ ๊ด๊ณ๋ฅผ ์ฐพ๋ ๊ณผ์ ์ด๋ค. ์ด๋ฐ ์์ธก ๋ชจ๋ธ๋ง์ ์ก์ ๊ฒ์ฌ ์๋ํ, ๋ถ๋ ์ ํ ์ฌ์ ํ์ง, ๊ณ ๋น์ฉ ๊ฒ์ฌ ๊ฒฐ๊ณผ ์ถ์ ๋ฑ ์ ์กฐ ์์คํ
์ ๋ฐ์ ๊ฑธ์ณ ํ์ฉ๋๋ค. ๋์ ์ฑ๋ฅ์ ์์ธก ๋ชจ๋ธ์ ๋ฌ์ฑํ๊ธฐ ์ํด์๋ ์์ง์ ๋ฐ์ดํฐ๊ฐ ํ์์ ์ด๋ค. ํ์ง๋ง ์ ์กฐ ์์คํ
์์ ์ํ๋ ์ข
๋ฅ์ ๋ฐ์ดํฐ๋ฅผ ์ํ๋ ๋งํผ ํ๋ํ๋ ๊ฒ์ ํ์ค์ ์ผ๋ก ๊ฑฐ์ ๋ถ๊ฐ๋ฅํ๋ค. ๋ฐ์ดํฐ ํ๋์ ์ด๋ ค์์ ํฌ๊ฒ ์ธ๊ฐ์ง ์์ธ์ ์ํด ๋ฐ์ํ๋ค. ์ฒซ๋ฒ์งธ๋ก, ๋ผ๋ฒจ๋ง์ด ๋ ๋ฐ์ดํฐ๋ ํญ์ ๋น์ฉ์ ์๋ฐํ๋ค๋ ์ ์ด๋ค. ๋ง์ ๋ฌธ์ ์์, ๋ผ๋ฒจ๋ง์ ์๋ จ๋ ์์ง๋์ด์ ์ํด ์ํ๋์ด์ผ ํ๊ณ , ์ด๋ ํฐ ๋น์ฉ์ ๋ฐ์์ํจ๋ค. ๋๋ฒ์งธ๋ก, ๊ฒ์ฌ ๋น์ฉ ๋๋ฌธ์ ๋ชจ๋ ๊ฒ์ฌ๊ฐ ๋ชจ๋ ์ ํ์ ๋ํด ์ํ๋ ์ ์๋ค. ์ ์กฐ ์์คํ
์๋ ์๊ฐ์ , ๊ธ์ ์ ์ ์ฝ์ด ์กด์ฌํ๊ธฐ ๋๋ฌธ์, ์ํ๋ ๋ชจ๋ ๊ฒ์ฌ ๊ฒฐ๊ณผ๊ฐ์ ํ๋ํ๋ ๊ฒ์ด ์ด๋ ต๋ค. ์ธ๋ฒ์งธ๋ก, ์ ์กฐ ํ๊ฒฝ์ ๋ณํ๊ฐ ๋ฐ์ดํฐ ํ๋์ ์ด๋ ต๊ฒ ๋ง๋ ๋ค. ์ ์กฐ ํ๊ฒฝ์ ๋ณํ๋ ์์ฑ๋๋ ๋ฐ์ดํฐ์ ๋ถํฌ๋ฅผ ๋ณํ์์ผ, ์ผ๊ด์ฑ ์๋ ๋ฐ์ดํฐ๋ฅผ ์ถฉ๋ถํ ํ๋ํ์ง ๋ชปํ๊ฒ ํ๋ค. ์ด๋ก ์ธํด ์ ์ ์์ ๋ฐ์ดํฐ๋ง์ผ๋ก ๋ชจ๋ธ์ ์ฌํ์ต์์ผ์ผ ํ๋ ์ํฉ์ด ๋น๋ฒํ๊ฒ ๋ฐ์ํ๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ด๋ฐ ๋ฐ์ดํฐ ํ๋์ ์ด๋ ค์์ ๊ทน๋ณตํ๊ธฐ ์ํด ๋ฅ๋ ํ์ต, ๋ฅ๋ ํผ์ณ๊ฐ ํ๋, ๋๋ฉ์ธ ์ ์ ๋ฐฉ๋ฒ์ ํ์ฉํ๋ค. ๋จผ์ , ์จ์ดํผ ๋งต ํจํด ๋ถ๋ฅ ๋ฌธ์ ์ ๋์ ๋ผ๋ฒจ๋ง ๋น์ฉ์ ํด๊ฒฐํ๊ธฐ ์ํด ๋ฅ๋ํ์ต ํ๋ ์์ํฌ๋ฅผ ์ ์ํ๋ค. ์ด๋ฅผ ํตํด ์ ์ ๋ผ๋ฒจ๋ง ๋น์ฉ์ผ๋ก ๋์ ์ฑ๋ฅ์ ๋ถ๋ฅ ๋ชจ๋ธ์ ๊ตฌ์ถํ ์ ์๋ค. ๋์๊ฐ, ๊ตฐ์ง ๋จ์์ ๋ผ๋ฒจ๋ง ๋ฐฉ๋ฒ์ ๋ฅ๋ํ์ต์ ์ ๋ชฉํ์ฌ ๋น์ฉ ํจ์จ์ฑ์ ํ์ฐจ๋ก ๋ ๊ฐ์ ํ๋ค. ์ ํ ๋ถ๋ ์์ธก์ ํ์ฉ๋๋ ๊ฒ์ฌ ๋น์ฉ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด์๋ ๋ฅ๋ ๊ฒ์ฌ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ ์ํ๋ ์๋ก์ด ๋ถํ์ค์ฑ ์ถ์ ๋ฐฉ๋ฒ์ ํตํด ๊ณ ๋น์ฉ ๊ฒ์ฌ ๋์ ์ ํ์ ์ ํํจ์ผ๋ก์จ ์ ์ ๊ฒ์ฌ ๋น์ฉ์ผ๋ก ๋์ ์ฑ๋ฅ์ ์ป์ ์ ์๋ค. ๋ฐ๋์ฒด ์ ์กฐ์ ์จ์ดํผ ๋ถ๋ ์์ธก์์ ๋น๋ฒํ๊ฒ ๋ฐ์ํ๋ ๋ ์ํผ ๋ณ๊ฒฝ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด์๋ ๋๋ฉ์ธ ์ ์ ๋ฐฉ๋ฒ์ ํ์ฉํ๋ค. ๋น๊ต์ฌ ๋๋ฉ์ธ ์ ์๊ณผ ๋ฐ๊ต์ฌ ๋๋ฉ์ธ ์ ์์ ์์ฐจ์ ์ธ ์ ์ฉ์ ํตํด ๋ ์ํผ ๋ณ๊ฒฝ์ ์ํ ์ฑ๋ฅ ์ ํ๋ฅผ ์ต์ํํ๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ค์ ๋ฐ์ดํฐ์ ๋ํ ์คํ์ ํตํด ์ ์๋ ๋ฐฉ๋ฒ๋ก ๋ค์ด ์ ์กฐ์์คํ
์ ๋ฐ์ดํฐ ํ๋ ๋ฌธ์ ๋ฅผ ๊ทน๋ณตํ๊ณ ์์ธก ๋ชจ๋ธ์ ์ฑ๋ฅ์ ๋์ผ ์ ์์์ ํ์ธํ์๋ค.1. Introduction 1
2. Literature Review 9
2.1 Review of Related Methodologies 9
2.1.1 Active Learning 9
2.1.2 Active Feature-value Acquisition 11
2.1.3 Domain Adaptation 14
2.2 Review of Predictive Modelings in Manufacturing 15
2.2.1 Wafer Map Pattern Classification 15
2.2.2 Fault Detection and Classification 16
3. Active Learning for Wafer Map Pattern Classification 19
3.1 Problem Description 19
3.2 Proposed Method 21
3.2.1 System overview 21
3.2.2 Prediction model 25
3.2.3 Uncertainty estimation 25
3.2.4 Query wafer selection 29
3.2.5 Query wafer labeling 30
3.2.6 Model update 30
3.3 Experiments 31
3.3.1 Data description 31
3.3.2 Experimental design 31
3.3.3 Results and discussion 34
4. Active Cluster Annotation for Wafer Map Pattern Classification 42
4.1 Problem Description 42
4.2 Proposed Method 44
4.2.1 Clustering of unlabeled data 46
4.2.2 CNN training with labeled data 48
4.2.3 Cluster-level uncertainty estimation 49
4.2.4 Query cluster selection 50
4.2.5 Cluster-level annotation 50
4.3 Experiments 51
4.3.1 Data description 51
4.3.2 Experimental setting 51
4.3.3 Clustering results 53
4.3.4 Classification performance 54
4.3.5 Analysis for label noise 57
5. Active Inspection for Fault Prediction 60
5.1 Problem Description 60
5.2 Proposed Method 65
5.2.1 Active inspection framework 65
5.2.2 Acquisition based on Expected Prediction Change 68
5.3 Experiments 71
5.3.1 Data description 71
5.3.2 Fault prediction models 72
5.3.3 Experimental design 73
5.3.4 Results and discussion 74
6. Adaptive Fault Detection for Recipe Transition 76
6.1 Problem Description 76
6.2 Proposed Method 78
6.2.1 Overview 78
6.2.2 Unsupervised adaptation phase 81
6.2.3 Semi-supervised adaptation phase 83
6.3 Experiments 85
6.3.1 Data description 85
6.3.2 Experimental setting 85
6.3.3 Performance degradation caused by recipe transition 86
6.3.4 Effect of unsupervised adaptation 87
6.3.5 Effect of semi-supervised adaptation 88
7. Conclusion 91
7.1 Contributions 91
7.2 Future work 94Docto
Deep CNN-Based Automated Optical Inspection for Aerospace Components
ABSTRACT
The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset
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A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry
Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Unionโs Horizon 2020 Research and Innovation Program
YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach
Shrinking pattern dimensions leads to an increased variety of defect types in
semiconductor devices. This has spurred innovation in patterning approaches
such as Directed self-assembly (DSA) for which no traditional, automatic defect
inspection software exists. Machine Learning-based SEM image analysis has
become an increasingly popular research topic for defect inspection with
supervised ML models often showing the best performance. However, little
research has been done on obtaining a dataset with high-quality labels for
these supervised models. In this work, we propose a method for obtaining
coherent and complete labels for a dataset of hexagonal contact hole DSA
patterns while requiring minimal quality control effort from a DSA expert. We
show that YOLOv8, a state-of-the-art neural network, achieves defect detection
precisions of more than 0.9 mAP on our final dataset which best reflects DSA
expert defect labeling expectations. We discuss the strengths and limitations
of our proposed labeling approach and suggest directions for future work in
data-centric ML-based defect inspection.Comment: 8 pages, 10 figures, accepted for the 38th EMLC Conference 202
AI/ML Algorithms and Applications in VLSI Design and Technology
An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations
Machine Learning in Manufacturing towards Industry 4.0: From โFor Nowโ to โFour-Knowโ
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, โFour-Knowโ (Know-what, Know-why, Know-when, Know-how) and โFour-Levelโ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments
A novel approach for wafer defect pattern classification based on topological data analysis
In semiconductor manufacturing, wafer map defect pattern provides critical
information for facility maintenance and yield management, so the
classification of defect patterns is one of the most important tasks in the
manufacturing process. In this paper, we propose a novel way to represent the
shape of the defect pattern as a finite-dimensional vector, which will be used
as an input for a neural network algorithm for classification. The main idea is
to extract the topological features of each pattern by using the theory of
persistent homology from topological data analysis (TDA). Through some
experiments with a simulated dataset, we show that the proposed method is
faster and much more efficient in training with higher accuracy, compared with
the method using convolutional neural networks (CNN) which is the most common
approach for wafer map defect pattern classification. Moreover, our method
outperforms the CNN-based method when the number of training data is not enough
and is imbalanced
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