•The paper surveys various optical inspection systems in the semiconductor industry.•The previous works are reviewed by the product categories and algorithm attributes.•The target inspection products ...include wafer, LCD, and LED.•Inspection algorithms include projection, filtering learning, and hybrid methods.•Total 56 previous systems are reviewed in this survey paper.
Automated visual inspection is an image-processing technique for quality control and production line automation. This paper reviews various optical inspection approaches in the semiconductor industry and categorize the previous literatures by the inspection algorithm and inspected products. The vision-based algorithms that had been adopted in the visual inspection systems include projection methods, filtering-based approaches, learning-based approaches, and hybrid methods. To discuss about the practical applications, the semiconductor industry covers the manufacturing and production of wafer, thin-film transistor liquid crystal displays, and light-emitting diodes. To improve the yield rate and reduce manufacturing costs, the inspection devices are widely installed in the design, layout, fabrication, assembly, and testing processes of production lines. To achieve a high robustness and computational efficiency of automated visual inspection, interdisciplinary knowledge between precision manufacturing and advanced image-processing techniques is required in the novel system design. This paper reviews multiple defect types of various inspected products which can be referenced for further implementations and improvements.
Precision measurement of large scale surface topography is a challenging task for high-reflective parts. This paper presents a multi-sensor system to address this issue by integrating the technical ...merits of touch trigger probe and photometric stereo. The probe is used to extract a set of high-precision sparse points for an initial surface construction, which is then applied to drive a near-field photometric stereo for recovering dense surface normal of the surface. By fusing the sparse discrete points with the dense surface normal, high-resolution surface topography can be obtained accordingly. Experiments confirm the state-of-the-art performance of the proposed method.
Deep learning approaches have recently raised the bar in many fields, from Natural Language Processing to Computer Vision, by leveraging large amounts of data. However, they could fail when the ...retrieved information is not enough to fit the vast number of parameters, frequently resulting in overfitting and therefore in poor generalizability. Few-Shot Learning aims at designing models that can effectively operate in a scarce data regime, yielding learning strategies that only need few supervised examples to be trained. These procedures are of both practical and theoretical importance, as they are crucial for many real-life scenarios in which data is either costly or even impossible to retrieve. Moreover, they bridge the distance between current data-hungry models and human-like generalization capability. Computer vision offers various tasks that can be few-shot inherent, such as person re-identification. This survey, which to the best of our knowledge is the first tackling this problem, is focused on Few-Shot Object Detection, which has received far less attention compared to Few-Shot Classification due to the intrinsic challenge level. In this regard, this review presents an extensive description of the approaches that have been tested in the current literature, discussing their pros and cons, and classifying them according to a rigorous taxonomy.
The world is currently facing an adverse condition due to the pandemic of airborne pathogen SARS-CoV-2. Prevention is better than cure; thus, the rapid detection of airborne pathogens is necessary ...because it can reduce outbreaks and save many lives. Considering the immense role of diverse detection techniques for airborne pathogens, proper summarization of these techniques would be beneficial for humans. Hence, this review explores and summarizes emerging techniques, such as optical and electrochemical biosensors used for detecting airborne bacteria (Bacillus anthracis, Mycobacterium tuberculosis, Staphylococcus aureus, and Streptococcus pneumoniae) and viruses (Influenza A, Avian influenza, Norovirus, and SARS-CoV-2). Significantly, the first section briefly focuses on various diagnostic modalities applied toward airborne pathogen detection. Next, the fabricated optical biosensors using various transducer materials involved in colorimetric and fluorescence strategies for infectious pathogen detection are extensively discussed. The third section is well documented based on electrochemical biosensors for airborne pathogen detection by differential pulse voltammetry, cyclic voltammetry, square-wave voltammetry, amperometry, and impedance spectroscopy. The unique pros and cons of these modalities and their future perspectives are addressed in the fourth and fifth sections. Overall, this review inspected 171 research articles published in the last decade and persuaded the importance of optical and electrochemical biosensors for airborne pathogen detection.
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•Conventional techniques involved in airborne pathogen detection are discussed.•Emerging optical biosensors for airborne pathogen detection are summarized.•The electrochemical biosensors for airborne pathogen detection are described.•The pros and cons of the optical and electrochemical biosensors are explained.
This study proposes the exploitation of deep learning for quantitative assessment of visual detectability of different types of in-service damage in laminated composite structures such as aircraft ...and wind turbine blades. A comprehensive image-based data set is collected from the literature containing common microscale damage mechanisms (matrix cracking and fibre breakage) and macroscale damage mechanisms (impact and erosion). Then, automated classification of the damage type and severity was done by pre-trained version of AlexNet that is a stable convolutional neural network for image processing. Pre-trained ResNet-50 and 5 other user-defined convolutional neural networks were also used to evaluate the performance of AlexNet. The results demonstrated that employing AlexNet network, using the relatively small image dataset, provided the highest accuracy level (87%–96%) for identifying the damage severity and types in a reasonable computational time. The generated knowledge and the collected image data in this paper will facilitate further research and development in the field of autonomous visual inspection of composite structures with the potential to significantly reduce the costs, health & safety risks and downtime associated with integrity assessment.
Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. ...Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images. This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection. The design of the architecture enables the model to be trained using a small number of samples, which is an important requirement for practical applications. The proposed model is compared with the related deep-learning methods, including the state-of-the-art commercial software, showing that the proposed approach outperforms the related methods on the specific domain of surface-crack detection. The large number of experiments also shed light on the required precision of the annotation, the number of required training samples and on the required computational cost. Experiments are performed on a newly created dataset based on a real-world quality control case and demonstrates that the proposed approach is able to learn on a small number of defected surfaces, using only approximately 25–30 defective training samples, instead of hundreds or thousands, which is usually the case in deep-learning applications. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited. The dataset is also made publicly available to encourage the development and evaluation of new methods for surface-defect detection.
Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This article ...attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: statistical, spectral, model-based, and machine learning. These works are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.
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