Semiconductor defects can have a major impact on the performance of electronic devices. To detect and analyze these defects, manufacturers use scanning electron microscopes (SEMs). However, traditional SEM image analysis methods are time-consuming and labor-intensive.
Recently, researchers have developed a new method called SEMI-PointRend to improve the accuracy and speed of SEM image analysis. This method uses deep learning and computer vision to automatically detect and classify defects in SEM images.
The first step of the SEMI-PointRend process is to create a 3D model of the sample. This model is then used to generate a point cloud, which is a set of points that represent the surface of the sample. The point cloud is then used to create a 3D representation of the sample, which is then used to detect and classify defects.
The second step of the process is to use a convolutional neural network (CNN) to identify and classify defects. The CNN is trained on a large dataset of SEM images, allowing it to accurately detect and classify defects.
Finally, the results of the CNN are used to generate a report that provides detailed information about the defects in the sample. This report can be used to improve the quality of semiconductor devices and reduce manufacturing costs.
Overall, SEMI-PointRend is a powerful tool for improving the accuracy and speed of SEM image analysis. It can help manufacturers quickly detect and classify defects in their products, allowing them to reduce costs and improve quality.
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- Source: Plato Data Intelligence: PlatoAiStream