In one embodiment, a method is provided. The method includes obtaining a sequence of images of a three-dimensional volume of a material. The method also includes determining a set of features based on the sequence of images and a first neural network. The set of features indicate microstructure features of the material. The method further includes determining a set of material properties of the three-dimensional volume of the material based on the set of features and a first transformer network.

A detection device for detecting the presence of a substance of interest in a sample is described. The device can include a data store comprising executable instructions for at least one convolutional neural network, CNN, configured to process images: and a processor coupled to the data store and configured to execute the instructions to operate the at least one CNN. The detection device can be configured to: obtain spectrometry data, operate a first one of the CNNs to process the spectrometry data to obtain a first CNN output; apply a mask to the spectrometry data to obtain masked data; operate a second one of the CNNs to process the masked data to obtain a second CNN output; and determine if the substance of interest is present in the sample based on both the first CNN output and the second CNN output.

A terahertz scanner for detecting irregularities, such as chemical or structural variations, in a sample and methods of use thereof are described. The described terahertz scanner and algorithms allow for direct, high-sensitivity, high-throughput, and non-invasive detection of irregularities that range from chemical contaminant to material defects in a variety of substrates and settings.

A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

A method for analyzing 2D material thin film and a system for analyzing 2D material thin film are disclosed. The detection method includes the following steps: capturing sample images of 2D material thin films; measuring the 2D material thin films by a Raman spectrometer; performing a visible light hyperspectral algorithm on the sample images by a processor to generate a plurality of visible light hyperspectral images; performing a training and validation procedure, performing an image feature algorithm on the visible light hyperspectral images, and establishing a thin film prediction model based on a validation; and capturing a thin-film image to be measured by the optical microscope, performing the visible light hyperspectral algorithm, and then generating a distribution result of the thin-film image to be measured according to an analysis of the thin film prediction model.

A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.

There is provided a technology that supports selection of a label to be used for analysis of target molecules. The present technology provides a label selection support system including an information acquisition unit that obtains, via a network, information associated with a plurality of target molecules to be analyzed, an information processor that obtains, using the information associated with a plurality of target molecules, in vivo expression information of the plurality of target molecules from a database storing in vivo expression information of target molecules and generates support information associated with assignment of a label to each of the plurality of target molecules on the basis of the expression information, and a transmitter that transmits the generated support information via the network.

An information processing system includes an irradiation unit configured to irradiate a deposited material with electromagnetic waves, a detection unit configured to detect scattered waves or transmitted waves of the electromagnetic waves with which the deposited material has been irradiated by the irradiation unit, and a determination unit configured to determine a state of the deposited material from an image based on the scattered waves or the transmitted waves detected by the detection unit.

Embodiments relate to a bioagent capture and identification system including a microfluidic platform for label-free, size-based capture, enrichment, and optical profiling of bioagents using vertically aligned carbon nanotubes coated in gold nanoparticles. Bioagent identification can be automated using machine learning. Captured bioagents remain viable after capture and analysis. In the nanotube fabrication process, catalyst precursor layers are fabricated using patterned stamps. In addition, nanotube diameter and density are increased by increasing the concentration of metal content in the catalyst precursor layer.

A method is provided for hyperspectral inversion of a phosphorus content of rubber tree leaves. The method includes: acquiring hyperspectral data of to-be-detected rubber tree leaves; extracting key wavelengths of the rubber tree leaves according to the hyperspectral data and a pre-established wavelength extraction model, where the key wavelengths are related to the phosphorus content of the rubber tree leaves, and the pre-established wavelength extraction model is obtained by learning and training hyperspectral sample data and sample phosphorus content data pairs in a pre-established sample database by adopting a competitive adaptive reweighted sampling (CARS) algorithm and a successive projection algorithm (SPA); and inputting the key wavelengths into a pre-established phosphorus content prediction model to calculate the phosphorus content of the to-be-detected rubber tree leaves. Moreover, the CARS algorithm and the SPA are comprehensively applied to extract the key wavelengths closely related to the phosphorus content of the rubber tree leaves.