Fei Comapny

Umesh Adiga

Mark Biedrzycki

Methods and systems for performing a hybrid machine learning method for enhancing scanning electron microscopy (SEM) images are disclosed herein. Methods include the steps of acquiring a plurality of images of a region of a sample that were each generated by irradiating the sample with a pulsed charged particle beam, upscaling each of the individual images to generate a plurality of upscaled images of the region of the sample, and combining the plurality of upscaled images to form a noise reduced image of the region of the sample.

Systems and methods for hybrid enhancement of scanning electron microscope images

Fiducial coordinates are obtained by aligning template with region of interest extracted from a workpiece image. Image values in the region of interest are projected along a template axis and the project values evaluated to establish a fiducial location which can be used as a reference location for locating workpiece areas for ion beam milling or other processing.

Shape invariant method for accurate fiducial finding

Apparatuses and methods for metrology on devices using fast marching level sets are disclosed herein. An example method at least includes initiating a fast marching level set seed on an image, propagating a fast marching level set curve from the fast marching level set seed to locate boundaries of a plurality of regions of interest within the image, and performing metrology on the regions of interest based in part on the boundaries.

Metrology of semiconductor devices in electron micrographs using fast marching level sets

Smart metrology methods and apparatuses disclosed herein process images for automatic metrology of desired features. An example method at least includes extracting a region of interest from an image, the region including one or more boundaries between different sections, enhancing at least the extracted region of interest based on one or more filters, generating a multi-scale data set of the region of interest based on the enhanced region of interest, initializing a model of the region of interest; optimizing a plurality of active contours within the enhanced region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest, and performing metrology on the region of interest based on the identified boundaries.

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Portland, OR, United States of America

Umesh Adiga