A spectroscopic analysis apparatus, a spectroscopic analysis method, and a program capable of appropriately analyzing a sample are provided. The spectroscopic analysis apparatus according to an embodiment includes: a light source (13) generates light to be incident on a sample including a plurality of substances labeled by a plurality of labeled substances; a spectrometer (14) disperse observed light generated in the sample by the light incident on the sample; a detector (15) detects the observed light dispersed by the spectrometer (14) to output observed spectral data; and a processor (16) analyzes the plurality of substances included in the sample based on the observed spectral data output from the detector (15), the processor (16) analyzing the substances included in the sample using a generalized inverse of a matrix having, as elements, reference spectral data set for the plurality of labeled substances and data of a noise component.

A method for optical imaging based on spectral shift assessment. The method includes generating a sample by mixing an object with a fluorophore, stimulating the sample by emitting a laser beam, extracting a plurality of fluorescence spectra from a plurality of fluorescence emissions emitted from the sample, detecting a plurality of fluorescence peaks and a plurality of peak wavelengths in the plurality of fluorescence spectra, extracting a plurality of fluorophore concentrations from a database, and generating a concentration image. The plurality of peak wavelengths are detected by detecting a respective peak wavelength of the plurality of peak wavelengths. Each of the plurality of fluorophore concentrations is associated with a respective peak wavelength of the plurality of peak wavelengths. The concentration image includes a first plurality of pixels. The concentration image is generated based on a respective fluorophore concentration of the plurality of fluorophore concentrations.

Instruments, systems, and methods for measuring optical density of microbiological samples are provided. In particular, optical density instruments providing improved safety, efficiency, comfort, and convenience are provided. Such optical density instruments include a handheld portion and a base station. The optical density instruments may be used in systems and methods for measuring optical density of biological samples.

A method, computer program product, and apparatus are provided for controlling components of a detection device. The device may detect turbidity of liquid with sensors such as a density sensor and/or nephelometric sensor. A light modulation pattern may reduce or eliminate interference in sensor readings. Readings may be performed during off cycles of an illumination light to reduce interference but to provide improved visibility of a tube. Dark and light sensor readings may be performed with an emitter respectively off or on to account for ambient light in subsequent readings. Readings from the density sensor and/or nephelometric sensor may be used to calculate McFarland values. The device may be zeroed based on an emitter level that results in a sensor reading satisfying a predetermined criterion.

Apparatuses and associated methods of manufacturing are described that provide a tip resistant optical testing instrument configured to rest on a surface. The optical testing instrument includes a shell defining a cavity for receiving a sample tube. The shell includes a bottom shell surface, wherein the bottom shell surface defines at least one support element, wherein the at least one support element is configured to engage the surface to support the optical testing instrument in a testing position, and a translational surface configured to engage the surface to support the optical testing instrument in an angled position. In an instance in which the optical testing instrument tilts from the testing position to the angled position, the translational surface is configured to engage the surface contacting the translational surface to prevent the optical testing instrument from tipping further and allow the optical testing instrument to return to the testing position.

A detection unit receives an optical signal that has passed through a space to be measured. A spectrum extraction unit extracts a range to be measured from the optical signal received by the detection unit. The spectrum extraction unit extracts an optical signal formed as a gas molecule of a gas to be measured absorbs energy of the optical signal. A determination unit determines the presence of an anomaly in the space to be measured based on a waveform of the optical signal extracted by the spectrum extraction unit.

A tomography method, system, and apparatus based on time-domain spectroscopy are provided. A light emitter is controlled to emit a pulse beam to scan a cross-section of an object to be measured while using a light receiver to detect the pulse beam passing through the object to be measured, so as to obtain time-domain pulse signals at locations of a scan path. A scan angle is repeatedly changed to perform the scanning and detecting steps, so as to collect the time-domain pulse signals of multiple angles of the cross-section as a time information set. Features are retrieved from the time-domain pulse signals using kernels of a trained machine learning model, which is trained with time information sets and corresponding ground truth images of cross-sections to learn the kernels for retrieving the features. The retrieved features are converted into a spatial domain to reconstruct a cross-sectional image of the object.

Methods and systems for training and implementing metrology recipes while dynamically controlling the convergence trajectories of multiple performance objectives are described herein. Performance metrics are employed to regularize the optimization process employed during measurement model training, model-based regression, or both. Weighting values associated with each of the performance objectives in the loss function of the model optimization are dynamically controlled during model training. In this manner, convergence of each performance objective and the tradeoff between multiple performance objectives of the loss function is controlled to arrive at a trained measurement model in a stable, balanced manner. A trained measurement model is employed to estimate values of parameters of interest based on measurements of structures having unknown values of one or more parameters of interest. In another aspect, weighting values associated with each of the performance objectives in a model-based regression on a measurement model are dynamically controlled.

A method for measuring a characteristic of a thin film is disclosed. The method includes a) obtaining a measured spectrum from a target region on the substrate by using a spectroscopic ellipsometer, b) obtaining a physical model capable of obtaining an estimated parameter value related to the characteristic of the thin film through regression analysis of the measured spectrum, c) obtaining a machine learning model capable of obtaining a reference parameter value related to the characteristic of the thin film by using the measured spectrum, and d) obtaining an integrated model which uses an integrated error function capable of considering both of a first error function and a second error function, and obtaining an optimum parameter value through regression analysis of the integrated model.

Apparatuses and methods for monitoring curing of photocurable material are disclosed. The methods generally include directing an ultraviolet cure light into a photocurable material, wherein the ultraviolet cure light causes the photocurable material to cure; directing a probe light into the photocurable material through an optical fiber during the cure; collecting a back reflection signal from the photocurable material with the optical fiber; and determining a refractive index change of the photocurable material during the cure.