2017
DOI: 10.1117/12.2260664
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CD-SEM distortion quantification for EPE metrology and contour analysis

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Cited by 3 publications
(3 citation statements)
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“…By computing the norm of the vector constructed with these two displacements, the centroid shift metric is obtained. To compensate for the impact of SEM image distortion, centroid shift values are corrected using a six parameters model [9], which is detailed in section 3.5. Then, Centroid Shift Uniformity (CSU) is calculated as the variance of the centroid shift values inside one image, enabling the placement's variability quantification.…”
Section: Contour-based Metrics Definitions and Remote Metrology Systemmentioning
confidence: 99%
“…By computing the norm of the vector constructed with these two displacements, the centroid shift metric is obtained. To compensate for the impact of SEM image distortion, centroid shift values are corrected using a six parameters model [9], which is detailed in section 3.5. Then, Centroid Shift Uniformity (CSU) is calculated as the variance of the centroid shift values inside one image, enabling the placement's variability quantification.…”
Section: Contour-based Metrics Definitions and Remote Metrology Systemmentioning
confidence: 99%
“…Regarding image distortion in SEM, methods have been proposed for measuring and calibrating image distortion in CD-SEMs. 12,13 The calibration of the imaging equipment does not affect throughput during inspection, but it cannot consider machine difference, distortion that varies depending on the sample (such as charging), or matching errors between the design layout and the SEM image. Because inspection precision is sensitive to misalignments with low-resolution images, it is necessary to predict misalignments between the design layout and the SEM image during inspection.…”
Section: Image Distortion Predictionmentioning
confidence: 99%
“…Regarding image distortion in SEM, methods have been proposed for measuring and calibrating image distortion in CD-SEMs 12 , 13 . The calibration of the imaging equipment does not affect throughput during inspection, but it cannot consider machine difference, distortion that varies depending on the sample (such as charging), or matching errors between the design layout and the SEM image.…”
Section: Trainable Die-to-database Inspectionmentioning
confidence: 99%