KEYWORDS: Scanning electron microscopy, Sensors, Electrons, 3D image processing, 3D vision, Reflectivity, 3D modeling, Image segmentation, Diffusion tensor imaging, Particles
As the industry moves to smaller design rules, shrinking process windows and shorter product lifecycles, the need for enhanced yield management methodology is increasing. Defect classification is required for identification and isolation of yield loss sources. Practice demonstrates that an operator relies on 3D information heavily while classifying defects. Therefore, Defect Topographic Map (DTM) information can enhance Automatic Defect Classification (ADC) capabilities dramatically.
In the present article, we describe the manner in which reliable and rapid SEM measurements of defect topography characteristics increase the classifier ability to achieve fast identification of the exact process step at which a given defect was introduced. Special multiple perspective SEM imaging allows efficient application of the photometric stereo methods. Physical properties of a defect can be derived from the 3D by using straightforward computer vision algorithms. We will show several examples, from both production fabs and R&D lines, of instances where the depth map is essential in correctly partitioning the defects, thus reducing time to source and overall fab expenses due to defect excursions.
Industrial applications related to semiconductor wafer fabrication, such as Automatic Defect Classification (ADC), require reliable and rapid measurements of defect characteristics, typically within one second or less. Generally, ADC makes intensive use of 2D defect descriptors such as size or texture. Incorporation of 3D defect descriptors into the classifier can improve the ability to get a fast identification of the exact process step at which a given defect was introduced. Such 3D descriptors can be readily elucidated form a Defect Topographic Map (DTM), which holds the defect's complete 3D representation. In this paper, we introduce an approach for obtaining the DTM form a combination of a non-Lambertian reflectance function with a photometric stereo method (PSM), based on Multiple Perspective SEM Imaging. We also introduce a rapid map generation method based on the Fast Fourier Transform, which we name the Fourier Defect Topographic Map (FDTM). The FDTM exhibits good spatial performance as well as reduced computational requirements. Finally, we discus the detrimental effects of SEM material contrast effects on the generation of topographic maps. We show that effects such as shadowing and noise, can be readily overcome when using MPSI images, resulting in high-quality 3D defect representation.
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