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.

Methods and systems for conducting tomographic imaging microscopy of a sample with a high energy charged particle beam include irradiating a first region of the sample in a first angular position with a high energy charged particle beam and detecting emissions resultant from the charged particle beam irradiating the first region. The sample is repositioned into a second angular position such that the second region to be different than the first region, and a second region of the sample is irradiated. Example repositioning may include one or more of a translation of the sample, a helical rotation of the sample, the sample being positioned in a non-eucentric position, or a combination thereof. Emissions resultant from irradiation of the second region are then detected, and a 3D model of a portion of the sample is generated based at least in part on the detected first emissions and detected second emissions.

Machine learning approach can combine mass spectral imaging (MSI) techniques, one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The machine learning approach can perform transformations on the spectral image data of the two MSI techniques to reduce dimensionality, and using a correlation technique, find relationships between the transformed spectral image data. The determined relationships can be used to generate MSI spectra of desired resolution.

A detector for imaging and efficiently digitizing a spatial distribution of photon flux includes pixel circuits that compressively encode pixel values generated by integrated analog to digital converters (ADCs). On-pixel digital compression circuits (DCCs) implement compression to increase continuous frame rate by reducing the number of bits per pixel while keeping quantization error below Poisson noise. Several mapping algorithms for photon-counting and charge-integrating detectors and compact digital logic implementations are presented.

Dynamic band contrast image (DBCI) is constructed with scattering patterns acquired at multiple scanning locations of a sample using a charged particle beam. Each pixel of the DBCI is generated by integrating the corresponding scattering pattern along a diffraction band. The DBCI includes charged particle channeling condition and can be used for detecting sample defects.

An approach is described that combines distinct properties of a specialized porous nitrocellulose film (PNC) with quantum nanoparticles to create an improved assay and detection sensitivity, permitting the development of a camera-based imaging system for fluorescent detection of macromolecules in microarray format. The two properties of PNC that facilitate the approach are an extraordinarily high binding capacity and a newly observed internal scattering of light. Quantum nanoparticles complement these PNC properties by providing a higher level of emitted light than the fluorescent dyes in common microarray use. Overall, the approach allows for instrument cost savings, reduced imaging time, and the ability to remotely image.

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.

Techniques are provided for tomographic imaging with sub-beam resolution and spectral enhancement. A system implementing the techniques according to an embodiment includes a target structure comprising one or more selected materials nanopatterned on a first surface of the target structure in a selected arrangement. The system also includes a primary particle beam source to provide a particle beam incident on an area of the first surface of the target structure, the area encompassing one or more of the nanopatterned materials, such that the materials generate characteristic X-rays in response to the primary beam. The system further includes a spectral energy detector (SED) to perform individual photon counting and spectral analysis of the characteristic X-rays and estimate attenuation properties of the imaged sample. The sample is positioned both adjacent to a second surface of the target structure, opposite the first surface, and between the target structure and the SED.

The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample, and scanning said charged particle beam over at least part of said sample. A first detector is used for obtaining measured detector signals corresponding to emissions of a first type from the sample at a plurality of sample positions. According to the method, a set of data class elements is provided, wherein each data class element relates an expected detector signal to a corresponding sample information value. The measured detector signals are processed, and processing comprises comparing said measured detector signals to said set of data class elements; determining at least one probability that said measured detector signals belong to a certain one of said set of data class elements; and assigning at least one sample information value and said at least one probability to each of the plurality of sample positions. Finally, sample information values and corresponding probability can be represented in data.