Spinel ferrites demonstrate extensive applications in different areas, like electrodes for electrochemical devices, gas sensors, catalysts, and magnetic adsorbents for environmentally important processes. However, defects in the real spinel structure can change the many physical and chemical properties of spinel ferrites. Although the number of defects in a crystal spinel lattice is small, their influence on the vast majority of physical properties could be really decisive. This review provides an overview of the structural characteristics of spinel compounds (e.g., CoFe2O4, NiFe2O4, ZnFe2O4, Fe3O4, γ–Fe2O3, Co3O4, Mn3O4, NiCo2O4, ZnCo2O4, Co2MnO4, etc.) and examines the influence of defects on their properties. Attention was paid to the classification (0D, 1D, 2D, and 3D defects), nomenclature, and the formation of point and surface defects in ferrites. An in-depth description of the defects responsible for the physicochemical properties and the methodologies employed for their determination are presented. DFT as the most common simulation approach is described in relation to modeling the point defects in spinel compounds. The significant influence of defect distribution on the magnetic interactions between cations, enhancing magnetic properties, is highlighted. The main defect-engineering strategies (direct synthesis and post-treatment) are described. An antistructural notation of active centers in spinel cobalt ferrite is presented. It is shown that the introduction of cations with different charges (e.g., Cu(I), Mn(II), Ce(III), or Ce(IV)) into the cobalt ferrite spinel matrix results in the formation of various point defects. The ability to predict the type of defects and their impact on material properties is the basis of defect engineering, which is currently an extremely promising direction in modern materials science. 

Studying the Defects in Spinel Compounds: Discovery, Formation Mechanisms, Classification, and Influence on Catalytic Properties

Poor chip solder joints can severely affect the quality of the finished printed circuit boards (PCBs). Due to the diversity of solder joint defects and the scarcity of anomaly data, it is a challenging task to automatically and accurately detect all types of solder joint defects in the production process in real time. To address this issue, we propose a flexible framework based on contrastive self-supervised learning (CSSL). In this framework, we first design several special data augmentation approaches to generate abundant synthetic, not good (sNG) data from the normal solder joint data. Then, we develop a data filter network to distill the highest quality data from sNG data. Based on the proposed CSSL framework, a high-accuracy classifier can be obtained even when the available training data are very limited. Ablation experiments verify that the proposed method can effectively improve the ability of the classifier to learn normal solder joint (OK) features. Through comparative experiments, the classifier trained with the help of the proposed method can achieve an accuracy of 99.14% on the test set, which is better than other competitive methods. In addition, its reasoning time is less than 6 ms per chip image, which is in favor of the real-time defect detection of chip solder joints.

https://www.mdpi.com/1099-4300/25/2/268/pdf?version=1675325216

A Novel Contrastive Self-Supervised Learning Framework for Solving Data Imbalance in Solder Joint Defect Detection

As an emerging consumer electronic product, the use of unmanned aerial vehicle(UAV) for a variety of tasks has received growing attention and favor in the enterprise or individual consumer electronics market in recent years. The deep neural network based object detectors are convenient to embed into the UAV product, however, the drone-captured images could bring the potential challenges of object occlusion, large scale difference and complex background to these methods because they are not desinged for the detection of small and tiny objects within the aerial images. To address the problem, we propose an improved YOLO paradigm called SR-YOLO with an Efficient Neck, Shape CIoU and Recursion Bottleneck Transformer for better object detection performance in consumer-level UAV products. Firstly, an efficient neck structure is presented to retain richer features through a small object detection layer and an up-sampling operator suitable for small object detection. Secondly, we design a new prediction box loss function called shape complete-IoU(SCIoU), which utilizes a width (height) limiting factor to alleviate the deficiency that the CIoU only focuses on aspect ratios by taking into account both the aspect ratio and the ratio of the two boxes’ widths. Moreover, combined with recurrent neural network and multi-head self-attention mechanism at the cyclic manner, a recursive bottleneck transformer is constructed to relieve the impact of highly dense scene and occlusion problems exists in UAV images. We conduct the extensive experiments on two public datasets of VisDrone2019 and TinyPerson, where the results show that the proposed model surpasses the compared YOLO by 8.1% and 3.2% in mAP50 respectively. In addition, the analysis and case study also validate our SR-YOLO’s superiority and effectiveness. 

Consumer-Centric Insights into Resilient Small Object Detection: SCIoU Loss and Recursive Transformer Network

Abstract We present an automation system for conditioning a scanning probe microscopy (SPM) probe into different states on a Si(111)–(7 × 7) surface at room temperature. Topography images representing multiple surface states and probe condition states divided into 11 categories and trained by a convolution neural network with an accuracy of 87% were used to estimate the effectiveness of the probe with an accuracy of 98%. We demonstrate the responsiveness of the method by experimentally reforming a probe into different conditions defined by preset categories. This system will promote advancements in autonomous SPM experiments at atomic scale and room temperature. 

Probe conditioning via convolution neural network for scanning probe microscopy automation

The advent of two-dimensional nanomaterials, a revolutionary class of materials, is marked by their atomic-scale thickness, superior aspect ratios, robust mechanical attributes, and exceptional chemical stability. These materials, producible on a large scale, are emerging as the forefront candidates in the domain of membrane-based gas separation. The concept of defect engineering in 2D nanomaterials has introduced a novel approach in their application for membrane separation, offering an effective technique to augment the performance of these membranes. Nonetheless, the development of customized microstructures in gas separation membranes via defect engineering remains nascent. Hence, this review is designed to serve as a comprehensive guide for the application of defect engineering in 2D nanomaterial-based membranes. It delves into the most recent developments in this field, encompassing the synthesis methodologies of defective 2D nanomaterials and the mechanisms underlying gas transport. Special emphasis is placed on the utilization of defect-engineered 2D nanomaterial-based membranes in gas capture applications. Furthermore, the paper encapsulates the burgeoning challenges and prospective advancements in this area. In essence, defect engineering emerges as a promising avenue for enhancing the efficacy of 2D nanomaterial-based membranes in gas separation, offering significant potential for advancements in membrane-based gas separation technologies.

Advancements in defect engineering of two-dimensional nanomaterial-based membranes for enhanced gas separation.

Deep learning based atomic defect detection framework for two-dimensional materials

Defect detection is essential in production lines to guarantee the quality of products. However, detecting tiny defects is difficult. Besides, as the variety of products increases, the variety of defects also increases. Models take much time to retrain. In this paper, we propose an “Incremental Meta Defect Detection (IMDD) System,” which utilizes incremental meta-learning to detect tiny defects. We decompose the model into feature pyramids and use feature alignment to improve the sensitivity of minor defects. Incremental learning utilizes knowledge distillation but this affects the learning of new categories, so the model is quickly adapted to new categories. We further combine incremental learning with meta-learning to increase the generality of the model. In experiments, the proposed model is 1.14 times more accurate than previous techniques. Therefore, the proposed system can enhance the ability to identify minor defects and quickly adapt to new defect types.

An Incremental Meta Defect Detection System for Printed Circuit Boards

The serial number is essential for product tracking in industries. Without serial numbers, the control of production is lost. Optical character recognition (OCR)—a technique for extracting characters' features and utilizing these features to recognize them in digital images—is widely used to identify serial numbers and improve product tracking efficiency. However, it is relatively challenging for OCR to adapt to digital images containing unclean environments in industries. Images taken in such environments can include various objects, making it hard for OCR models to focus on serial numbers. In this paper, we propose an industrial product serial number recognition framework, which can efficiently adapt complex structures of the background environment and recognize serial numbers in industries. The framework includes the data preprocessing stage, detection stage, and recognition stage. Eventually, our framework reaches nearly four times better on the testing dataset than the baseline score and increases the efficiency of product tracing in industries.

An Efficient Industrial Product Serial Number Recognition Framework

Automated optical inspection (AOI) is essential in the electronic manufacturing production line. Strict screening rules lead to a high false alarm rate of AOI. Many industries use AI models to classify defects. The lack of flawed data and the uneven distribution of categories is a big challenge for model training. Furthermore, the AI model must be retrained when adding new production line data, and the time cost is high. In order to reduce the false alarm rate and improve the generalization of the AI model, we build a deep learning- based generic solder defect detection system (GSDD) to classify defects into seven types. In GSDD, the color gradation adjustment module solves the problem of color difference, and the data augmentation module solves the problem of variable data. In the experiment, we use the data set provided by the enterprise to evaluate the accuracy of the model to 96%, and the model can be applied to different machines. Thus, GSDD is a general model and can efficiently detect defects.

A Deep Learning-based Generic Solder Defect Detection System

There have been many related security issues about Artificial Intelligence (AI) in recent years. During the manufacturing process, products are captured by images for defect detection. If attackers use the model inversion attack to attack the AI model, the input image can be roughly restored, resulting in product information leakage. In this paper, we propose a system that confuses input images and uses them to train the model. Experiments show that our model has a high accuracy of 94.4% in defect image classification. Thus, the proposed system can achieve product information protection and accurate defect detection.

Image Confusion Applied to Industrial Defect Detection System

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