The first parameter showed a wide variation that could be divided into two groups: one lasting more than 40 s in 8 patients and another below 20 s in 103 patients. Since the time constant of the positional nystagmus induced by cupulolithiasis was much longer than that induced by canalolithiasis, this finding suggests that cupulolithiasis in the PSCC induced the vertical-torsional positional nystagmus with a long time constant in the group of eight patients. The vertical-torsional positional nystagmus disappeared in these patients at the neutral head position, where the axis of the cupula of affected PSCC aligned with gravity.
The local field effects on voltage contrast in the scanning electron microscope (SEM) mean that local fields generated by a non-uniform potential distribution on specimen surface cause a variation in the secondary electron (SE) detector current. It causes some errors in the voltage measurement. The authors present a theory to calculate the SE detector current in the presence of the local fields. In the calculation, they assume that the field distribution above the specimen surface (metal electrodes with 8 mu m width and 12 mu m separation) is two-dimensional. Analysed models are a conventional SEM detector system (model A), a retarding-field energy analyser with an extraction field (model B) and a high-resolution energy analyser with an extraction field (model C). The results show that the local field effects could not be neglected even in models B and C with strong extraction fields. The calculated values of local field effects in models A and B agree well with the experimental ones. The dependence of local field effects on the electrode geometry is equivalent to that on the extraction field, though this dependency is not so strong.
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%.
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