Automatic recognition of defect areas on a semiconductor wafer using multiple scanning electron microscope images

A technique for high-precision and automatic recognition of defect areas on a semiconductor wafer using scanning electron microscope (SEM) images is proposed. The proposed technique inputs multiple SEM images formed by selectively detecting secondary electrons and backscattered electrons emitted from the specimen by irradiating with primary electrons, and defect areas are then automatically recognized by comparison with reference images. The number of detected secondary electrons and backscattered electrons is highly dependent on the surface roughness of the defect areas, namely the height and depth of defects; therefore, a surface-roughness analysis from input images is conducted and the result is used to determine the mixing proportion for multiple difference images. The proposed technique aims to obtain high recognition accuracy for process wafers that contain various kinds of defects with a wide variety of height and depth. The technique provides effective pre-processing for automating the classification of defects, and is expected to contribute to improvements to the efficacy of process monitoring and yield management in the fabrication of semiconductor devices. Experimental results with two process wafers (involving 200 defect samples, each of which belongs to one of the nine defect classes) have confirmed that the proposed technique is capable of automatic recognition of defect areas with an accuracy of 98.9%.