The present disclosure relates to a method includes generating ions with an ion source of an ion implantation apparatus based on an ion implantation recipe. The method includes accelerating the generated ions based on an ion energy setting in the ion implantation recipe and determining an energy spectrum of the accelerated ions. The method also includes analyzing a relationship between the determined energy spectrum and the ion energy setting. The method further includes adjusting at least one parameter of a final energy magnet (FEM) of the ion implantation apparatus based on the analyzed relationship.

A focused ion beam (FIB) is used to mill beam spots into a substrate at a variety of ion beam column settings to form a set of training images that are used to train a convolutional neural network. After the neural network is trained, an ion beam can be adjusted by obtaining spot image which is processed with the neural network. The neural network can provide a magnitude and direction of defocus, aperture position, lens adjustments, or other ion beam or ion beam column settings. In some cases, adjustments are not made by the neural network, but serve to indicate that the ion beam and associated ion column continue to operate stably, and additional adjustment is not required.

In one embodiment, a multi charged particle beam evaluation method includes writing a plurality of evaluation patterns on a substrate by using multi charged particle beams, with a design value of a line width changed by a predetermined change amount at a predetermined pitch, measuring the line widths of the plurality of evaluation patterns thus written, and extracting a variation in a specific period of a distribution of differences between results of a measurement value and the design value of each of the line widths of the plurality of evaluation patterns. The predetermined change amount is equal to or larger than data resolution and smaller than a size of each of pixels, each of which is a unit region to be irradiated with one of the multi charged particle beams.

A standard reference plate is configured for insertion into an additive manufacturing apparatus for calibrating an electron beam of the additive manufacturing apparatus. The standard reference plate includes a lower plate and an upper plate being essentially in parallel and attached spaced apart from each other, the upper plate including a plurality of holes. A predetermined hollow pattern is provided inside the holes, and a spacing between the holes and the size of the holes and a distance between the upper plate and the lower plate and a position of an x-ray sensor of the additive manufacturing apparatus with respect to the standard reference plate are arranged so that x-rays emanating from the lower plate, when the electron beam is passing through a hollow part of the hollow pattern, will not pass directly from the lower plate through any one of the holes to the x-ray sensor.

A method of pattern data preparation includes the following steps. A desired pattern to be formed on a surface of a layer is inputted. A first set of beam shots are determined, and a first calculated pattern on the surface is calculated from the first set of beam shots. The first calculated pattern is rotated, so that a boundary of the desired pattern corresponding to a non-smooth boundary of the first calculated pattern is parallel to a boundary constituted by beam shots. A second set of beam shots are determined to revise the non-smooth boundary of the first calculated pattern, thereby calculating a second calculated pattern being close to the desired pattern on the surface. The present invention also provides a method of forming a pattern in a layer.

Methods for processing of a workpiece are disclosed. The actual rate at which different portions of an ion beam can process a workpiece, referred to as the processing rate profile, is determined by measuring the amount of material removed from, or added to, a workpiece by the ion beam as a function of ion beam position. An initial thickness profile of a workpiece to be processed is determined. Based on the initial thickness profile, a target thickness profile, and the processing rate profile of the ion beam, a first set of processing parameters are determined. The workpiece is then processed using this first set of processing parameters. In some embodiments, an updated thickness profile is determined after the first process and a second set of processing parameters are determined. A second process is performed using the second set of processing parameters. Optimizations to improve throughput are also disclosed.

The present disclosure relates to a method includes generating ions with an ion source of an ion implantation apparatus based on an ion implantation recipe. The method includes accelerating the generated ions based on an ion energy setting in the ion implantation recipe and determining an energy spectrum of the accelerated ions. The method also includes analyzing a relationship between the determined energy spectrum and the ion energy setting. The method further includes adjusting at least one parameter of a final energy magnet (FEM) of the ion implantation apparatus based on the analyzed relationship.

A multi-column assembly for a scanning electron microscopy (SEM) system is disclosed. The multi-column assembly includes a plurality of electron-optical columns arranged in an array defined by one or more spacings. Each electron-optical column includes one or more electron-optical elements. The plurality of electron-optical columns is configured to characterize one or more field areas on a surface of a sample secured on a stage. The number of electron-optical columns in the plurality of electron-optical columns equals an integer number of inspection areas in a field area of the one or more field areas. The one or more spacings of the plurality of electron-optical columns correspond to one or more dimensions of the inspection areas.

A method for process analysis includes acquiring first inspection data, using a first inspection modality, with respect to a substrate having multiple instances of a predefined pattern of features formed thereon using different, respective sets of process parameters. Characteristics of defects identified in the first inspection data are processed so as to select a first set of defect locations in which the first inspection data are indicative of an influence of the process parameters on the defects. Second inspection data are acquired, using a second inspection modality having a finer resolution than the first inspection modality, of the substrate at the locations in the first set. The defects appearing in the second inspection data are analyzed so as to select, from within the first set of the locations, a second set of the locations in which the second inspection data are indicative of an optimal range of the process parameters.

A focused ion beam (FIB) is used to mill beam spots into a substrate at a variety of ion beam column settings to form a set of training images that are used to train a convolutional neural network. After the neural network is trained, an ion beam can be adjusted by obtaining spot image which is processed with the neural network. The neural network can provide a magnitude and direction of defocus, aperture position, lens adjustments, or other ion beam or ion beam column settings. In some cases, adjustments are not made by the neural network, but serve to indicate that the ion beam and associated ion column continue to operate stably, and additional adjustment is not required.