Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: Methods and apparatus are disclosed for integration of image-based metrology into a milling workflow. A first ion beam milling operation is performed to an edge at a distance from a final target position on a sample. An SEM image of the sample is used to determine a distance between the milled edge and a reference structure on the sample. Based on the determined distance, the ion beam is adjusted to perform a second milling operation to shift the milled edge to the final target position. Extensions to iterative procedures are disclosed. Various geometric configurations and corrections are disclosed. Manufacturing and analytic applications are disclosed in a variety of fields, including read-write head manufacture and TEM sample preparation. Other combinations of imaging and milling tools can be used.

Abstract: Apparatuses and methods directed toward endpoint detection are disclosed herein. An example method at least includes forming a plurality of lines on a top surface of a sample; removing, a plurality of times, material from a working surface of the sample, the working surface different than the top surface; imaging, a plurality of times, the sample to at least capture the plurality of lines; and determining an endpoint based on a relative spatial characteristic between two or more lines of the plurality of lines.

Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: Apparatuses and methods directed toward endpoint detection are disclosed herein. An example method at least includes forming a plurality of lines on a top surface of a sample; removing, a plurality of times, material from a working surface of the sample, the working surface different than the top surface; imaging, a plurality of times, the sample to at least capture the plurality of lines; and determining an endpoint based on a relative spatial characteristic between two or more lines of the plurality of lines.

Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: The backside of a planar view lamella is prepared from a sample extracted from a workpiece. The sample includes multiple device layers and a substrate layer. After removing at least a part of the substrate layer covering a final device layer to obtain a sample surface, a region of interest (ROI) relative to the sample surface is alternately scanned with an electron beam and spontaneously etched until the final device layer within the ROI is exposed. One or more device layers may be removed from the sample backside after the final device layer is exposed to obtain the backside of the planar view lamella.

Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.