Disclosed herein is an electron-optical assembly testing system for testing an electron-optical assembly, the system comprising: a source of charged particles configured to emit a beam of charged particles; an electron-optical assembly holder configured to hold an electron-optical assembly to be tested such that, when the system is in use with an electron-optical assembly held by the electron-optical assembly holder, the electron-optical assembly is illuminated by the beam; and a sub-beam detector for detecting sub-beams of charged particles that have been transmitted through the electron-optical assembly.

The present disclosure is related to a Schottky thermal field (TFE) source for emitting an electron beam. Electron optics can adjust a shape of the electron beam before the electron beam impacts a scintillator screen. Thereafter, the scintillator screen generates an emission image in the form of light. An emission image can be adjusted and captured by a camera sensor in a camera at a desired magnification to create a final image of the Schottky TFE source's tip. The final image can be displayed and analyzed to for defects.

An improved method and apparatus for enhancing an inspection image in a charged-particle beam inspection system. An improved method for enhancing an inspection image comprises acquiring a plurality of test images of a sample that are obtained at different landing energies, determining distortion levels for the plurality of test images, determining a landing energy level that enables the sample to be in a neutral charge condition during inspection based on the distortion levels, and acquiring an inspection image based on the determined landing energy level.

Systems and methods for image enhancement are disclosed. A method for enhancing an image may include receiving records of a performance metric for beams of the multi-beam system in an imaging process, each record associated with a beam. The method may also include determining whether an abnormality of a beam occurs based on a baseline value determined using a portion of the records. The method may further include providing an abnormality indication in response to the determination that the abnormality has occurred.

The present disclosure is related to a Schottky thermal field (TFE) source for emitting an electron beam. Electron optics can adjust a shape of the electron beam before the electron beam impacts a scintillator screen. Thereafter, the scintillator screen generates an emission image in the form of light. An emission image can be adjusted and captured by a camera sensor in a camera at a desired magnification to create a final image of the Schottky TFE source's tip. The final image can be displayed and analyzed to for defects.

The disclosure relates to a method of aligning a charged particle beam apparatus, comprising the steps of providing a charged particle beam apparatus in a first alignment state; using an alignment algorithm, by a processing unit, for effecting an alignment transition from said first alignment state towards a second alignment state of said charged particle beam apparatus; and providing data related to said alignment transition to a modification algorithm for modifying said alignment algorithm in order to effect a modified alignment transition.

A method for evaluating a secondary optical system of an electron beam inspection device provided with a primary optical system that irradiates a sample placed at an observation target position with an electron beam emitted from an electron source, and the secondary optical system that forms, on a detector, an enlarged image of an electron beam generated from the sample or an electron beam transmitted through the sample. The method includes: placing a photoelectric surface at the observation target position; irradiating the photoelectric surface with laser; forming an enlarged image of an electron beam generated from the photoelectric surface on the detector by the secondary optical system; and evaluating the secondary optical system based on an electron beam image obtained by the detector.

An electron gun includes a cathode that is heated to emit thermions; a cathode heating power supply that supplies a cathode heating current for heating the cathode; a grid that has a first aperture formed therein and that has a grid voltage applied thereto, the grid voltage having a potential lower than that of the cathode, wherein the grid converges the thermions passing through the first aperture by the grid voltage; an anode that has a second aperture formed therein and that has an anode voltage applied thereto, wherein the anode causes the thermions extracted from the cathode to pass through the second aperture as an electron beam by the anode voltage; an anode-voltage power supply that applies the anode voltage to the anode; and a controller that causes the anode voltage having a positive potential to be applied from the anode-voltage power supply to the anode.

A charged particle beam microscope system is operated in a transmission imaging mode. During the operation, the charged particle beam microsystem directs a charged particle beam to the sample to produce images. A time series of beam tilts is applied in a pattern to the charged particle beam directed to the sample to produce a sequence of images. At least some of the images in the sequence of images are captured while the charged particle beam is transitioning between one beam tilt in the time series of beam tilts and a sequentially adjacent beam tilt in the time series of beam tilts. The pattern is configured to induce image changes between the images in the sequence of images that are indicative of optical aberrations in the charged particle beam microscope system.

A micro-tooling device, such as, for example, a scanning electron microscope or a focused-ion beam microscope, provides images. A first machine-learning algorithm and a second machine-learning algorithm are sequentially coupled. The first machine-learning algorithm determines a progress along a predefined workflow based on feature recognition in images associated with the workflow. The second machine-learning algorithm predicts settings of operational parameters of the micro-tooling device in accordance with the progress along the predefined workflow.