AI-Based Inline Metrology for Defect Detection in Solar Cells

A system for detecting defects in images of a specimen, comprising an ensemble of deep learning models and a pseudo-loss function, wherein the ensemble is trained with a training dataset and parameters are adjusted until the pseudo-loss function reaches a threshold of approximately 0.5, and wherein the trained ensemble is used to detect defects in runtime specimen images.

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A substrate inspection method and device that improves defect detection performance by acquiring multiple images of a substrate under different optical conditions, generating difference images and difference-of-difference images, and detecting low-SNR defect candidates through a multi-image analysis approach.

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A system for determining information for a specimen, such as classifying defects or predicting structure characteristics, that uses a combination of local and global texture features. The system determines a global texture characteristic of an image of the specimen and one or more local characteristics of a localized area in the image, and then inputs these features into a machine learning model to determine the specimen information. The global texture characteristic captures patterns that are present across the entire image, while the local characteristics capture patterns in specific regions. The system can be used for both metrology and inspection applications, and can improve classification performance by incorporating global texture information that is typically neglected by traditional feature generation techniques.

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Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detecting defects (mismatch), general defects (bubble, glass-crack and cell-crack) and easy-detecting defects (glass-upside-down). Corresponding to different types of defects, the deep learning model with different optimization methods and a classification detection method based on multi-models fusion are proposed in the paper. In the proposed model, in order to solve the mismatch problem between the default anchor boxes size of YOLOv5s model and the extreme scale of the battery mismatch defect label boxes, the K-means algorithm was adopted to re-cluster the dedicated anchor boxes for the mismatch defect label boxes. In order to improve the comprehensive detection accuracy of YOLOv5s model for the general defects, the YOLOv5s model was also improved by the methods of image preprocessing, anchor box improving and detection head replacing. In ord... Read More

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A method and system for evaluating defective areas of semiconductor substrates, comprising: enhancing the substrate image through a multi-level neural network; segmenting the enhanced image into clusters based on pixel features; and identifying defect locations through probe-based excitation and marking.

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A solar wafer defect detection system using deep learning that detects various defects including cracks, scratches, LCOs, hotspots, injectors, and LDSEs in solar wafers. The system employs a defect detection deep learning model and a black spot classification deep learning model to analyze electroluminescence images of the wafers. The defect detection model is trained on labeled images of defective wafers, while the black spot classification model identifies black spots in the images. The system provides real-time detection and classification of defects, enabling early detection and prevention of wafer failures.

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A semiconductor fabrication inspection system that uses a classification CNN to determine wafer defects, trained with a novel encoder-decoder architecture that generates soft labels through clustering of feature vectors. The system captures wafer images and classifies them into multiple defect types, with the training process utilizing multiple images to optimize the encoder, decoder, and classification CNN.

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A method for defect inspection in semiconductor fabrication that compensates for noise in current process steps by subtracting a noise image from a previous process step. The method involves generating images of sample regions after each process step, identifying a test region and comparison regions, and generating a multi-step difference image by weighted subtraction of previous process step images from the current process step image. Defects are then identified in the test region based on the multi-step difference image.

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Abstract The increasing production of solar cells, resulting from the rapid development of new energy sources, necessitates their inspection during both solar cell production and photovoltaic power plant inspection. Target detection algorithms are widely utilized for defect detection in solar cells. To achieve more accurate detection of minor defects in electroluminescent solar cells, an improved algorithm called CSR-YOLOv5s is proposed in this paper. The CSR-YOLOv5s combines Decoupled Head and CSRBlock with the YOLOv5s baseline model. The CSR-Y OLOv5s demonstrates a 1.1% increase in accuracy and a 2.1% increase in F1-score compared to the YOLOv5s baseline model, resulting in improved accuracy and recall. The algorithm effectively identifies minor defects in electroluminescent solar cells.

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Renewable energy resources such as solar energy, biomass, tidal, geothermal, and hydroelectric energy are becoming increasingly important due to their potential to mitigate the negative impacts of climate change and reduce our dependence on finite and polluting fossil fuels. Solar power can provide a clean, sustainable, and reliable source of renewable energy. Important component of solar power generation is the silicon panel and its surface quality is highly related to its robustness and power generation efficiency. Cell breakages resulting from micro-cracks, degradation and shunted areas on cells are proven to cause major issues and these affect the photovoltaic module efficiency and performance. Solar cell defect identification is important because defects in solar cells significantly reduce their efficiency, which in turn affects their power output and lifespan. By identifying and classifying defects during the production of these cells, engineers and researchers can improve the quality control of solar cells, leading to more reliable and efficient solar energy systems. The propo... Read More

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This paper proposed an improved model based on YOLOv7, with the aim of tackling the issues of low detection accuracy and heavy model weight of current defect detection algorithms for photovoltaic cells. Ghost convolution is introduced in the original backbone network and neck to reduce the weight of the model. Secondly, the DenseBlock module is improved in two parts to enhance feature extraction ability and feature fusion ability. The introduction of a Wise-IOU loss function to further enhance the detection accuracy of the model was finally successful. Experiments with four defect datassets featuring cracks, broken gates, thick lines and black cores revealed that the weight size of the improved model in this paper was reduced by 24.4%, and the defect detection accuracy and mean average detection accuracy were both increased by 9.7% and 3.7% respectively, in comparison to the original YOLOv7 network.

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In the production process of solar cells, inevitable faults such as cracks, dirt, dark spots, and scratches may occur, which could potentially impact the lifespan and power generation efficiency of solar cells. Addressing this issue, this paper combines neural networks with photoluminescence detection technology and proposes a novel neural network model for the classification and grading of defects in solar cells. Firstly, the YOLOv5 model is optimized and adjusted for algorithm and network structure. The optimization process is divided into three parts: global optimization of the network structure, optimization of the neck network structure, and optimization of the head structure, each addressing specific issues in recognition, detection, and classification. The impact of the optimized network model on recognition and detection speed is analyzed, and solutions are proposed to address any observed effects. Additionally, an iterative update of neural network hyperparameter combinations is performed for solar cell defect identification. Finally, using the ultimately optimized model str... Read More

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One way of examining surface defects on photovoltaic modules is the Electroluminescence (EL) imaging technique. The data set used in this work is an open data set for fault detection and classification of photovoltaic cells. In this article, we have used various deep learning (DL) techniques to ensure fault detection and diagnosis of photovoltaic modules. A binary classification model was developed that highlighted defective PV modules and normal modules. The subset of defective PV modules was used to design a multi-class model of default detection (light, moderate, and severe). Evaluation results (confusion matrix, mean square error (mse)) showed that methods based on deep learning performed exceptionally well, making it possible to solve the problem of detecting and diagnosing faults in photovoltaic modules with good overall precision (mse = 0.060).

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The importance of solar energy as a renewable power source has led to increased adoption of solar modules for electricity generation. However, faults in solar cells can significantly impact their performance and efficiency. Manual defect detection is time-consuming and subjective, hence the need for an intelligent and efficient detection solution. In this study, we propose a method for detecting defective solar cells in electroluminescence imaging using an advanced object detection algorithm, specifically YOLO5 version. An important step in the algorithm is to formulate the detection problem in terms of real-time detection of defects. We evaluate our method on a dataset of different types of solar modules containing a total of 240 solar cells with various defects, including finger interruptions, microcracks, electrically separated or degraded cell parts and material defects. Experimental evaluation on solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules datasets reveals that the proposed framework successfully mitigates the influence of ... Read More

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This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values ... Read More

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Silicon has been one among a few key technologically important materials especially in our modern world. Silicon, especially in its current most useful forms (thin layers), is a brittle material by nature. Thin silicon cells crack easily during manufacturing assembly. Every crack is a defect in any PV manufacturing lines, and if let to grow/propagate it will lead to reliability and quality issues with time. Nevertheless, the global silicon solar PV industry is aggressively reducing the cost of solar PV systems through cutting down the thickness of silicon solar cells. Cell cracks immediately lower PV module efficiency and can lead to premature aging of the entire module. The Crack Catcher AI was our research joint collaborations entry in the Department of Energy (DOE)s American-Made Solar Innovation competition in 2022 (Round 6) which later was selected as the national semifinalists and won an award announced by DOE in December 2022. It uses smart stress sensing and smart fracture prediction approaches utilizing fundamental fracture mechanics and big data analytics to reduce crac... Read More

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This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images, annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIB) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision ([email protected]) of 98.5%. Detailed analysis of the model's performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as thick line and star crack, the model maintained high performance with accuracies of 94% and 96%, respectively. The recall-confidence and precision-recall curves further demonstrate the model's robustness and reliability across varying confidence thresholds. This research not only advances... Read More

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Renewable energy resources can play an important role in significantly offsetting the power demand in most of the world. Being the lowest-cost renewable resource, investment in photovoltaic solar can be prioritized if the reliability of continuous generation is guaranteed. Micro defects in PV cells can reduce the electrical output and if not detected can lead to large-scale power distributions. Therefore, electroluminescence (EL) image is commonly used these days to detect these micro defects and classify the various types of these defects. Hence, a hybrid approach consisting of Alibi detection algorithm is proposed, which makes use of a deep learning-based approach to detect abnormal solar cells. And decision tree is employed for the previously obtained outlier instance score to further classify it into the different kinds of defects present in the EL image of the solar cells. The proposed model is tested on the ELPV image dataset consisting of mono conventional crystalline silicon cells with above 97% accuracy.

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A method for detecting defects in semiconductor wafers during processing, comprising capturing a test image of the wafer, generating a simulated integrated circuit layout by analyzing the test image, and identifying defects in the wafer based on the simulated layout. The simulated layout is generated using a machine learning process trained on reference layouts, enabling detection of defects in complex wafer features through comparison with a reference layout.

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Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems... Read More

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