Abstract: A method of method of operating a multibeamlet charged particle device is disclosed. In the method, a target attached to a stage is translated, and each step of selecting beamlets, initializing beamlets, and exposing the target is repeated. The step of selecting beamlets includes passing a reconfigurable plurality of selected beamlets through the blanking circuit. The step of initializing beamlets includes pointing each of the selected beamlets in an initial direction. The step of exposing the target includes scanning each of the selected beamlets from the initial direction to a final direction, and irradiating a plurality of regions of the target on the stage with the selected beamlets.

Abstract: Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

Abstract: A method of method of operating a multibeamlet charged particle device is disclosed. In the method, a target attached to a stage is translated, and each step of selecting beamlets, initializing beamlets, and exposing the target is repeated. The step of selecting beamlets includes passing a reconfigurable plurality of selected beamlets through the blanking circuit. The step of initializing beamlets includes pointing each of the selected beamlets in an initial direction. The step of exposing the target includes scanning each of the selected beamlets from the initial direction to a final direction, and irradiating a plurality of regions of the target on the stage with the selected beamlets.

Abstract: Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

Abstract: Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.