The surface identification of integrated circuit (IC) has not been standardized yet, and the task of detecting IC logos is often carried out manually, which is prone to high subjectivity and low efficiency. Traditional machine learning methods still rely on prior knowledge and handcrafted features, which yield unsatisfactory performance. In order to improve the efficiency of logo detection tasks, this paper constructs a dataset for IC appearance and proposes a deep learning algorithm based on the latest single-stage object detection neural network YOLOv8. By introducing Global Attention Mechanism(GAM) into YOLOv8, the information diffusion is effectively reduced, and the deep information is extracted, allowing the neural network to learn more representative features and further improve the average precision.
KEYWORDS: Image segmentation, Deep learning, Visual process modeling, Medical imaging, Image processing, Semantics, Performance modeling, Data modeling, Transformers, Feature extraction
Image segmentation remains a challenging problem in computer vision. Various segmentation methods have been developed, including traditional methods based on threshold, edge, region, and morphology, as well as novel methods based on deep learning. The segmentation precision is continuously improving, and its applications are expanding. Starting with traditional image segmentation methods, we organized their performance and practical application in relation to the demand for segmentation of IC surface defects. We then tested the segmentation of the IC surface defects database we created. However, the final result was unsatisfactory. Therefore, we are continuing to explore segmentation models based on deep learning and summarise their performance evaluation to explore the possibility of applying them to the segmentation of IC surface defects.
Scanning Acoustic Microscopy (SAM) is an essential tool for the non-destructively deriving depth-specific information, which can detect the internal morphology and defect location of Integrated Circuit (IC) sensitively. When SAM equipment works, it will generate a large number of acoustic scanning images, which provide data support for defect detection algorithm. However, due to lack of professional researchers to uniformly sort and classify these acoustic scanning data and the deficiency of standard and available acoustic scanning image datasets, it is impossible to carry out the research on intelligent detection algorithm. In order to solve the above problems, a novel Multi-category Scanning Acoustic Image (MSAI) database is presented in this paper. MSAI database including 4565 acoustic scanning images acquired from 52 products in four packaging structures (BGA, QFN, SOIC and SOP). In order to prove the availability of MSAI database, four typical convolutional neural networks are used to identify and classify the MSAI database. The experimental results show that the VGG-16 model achieves the best classification performance in IC packaging structures grading, which show a train accuracy of 99.58% and a test accuracy of 99.44%, and all network models show a good inter-class separability on MSAI database.
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