The present invention relates to the use of Raman spectroscopy for the monitoring and assessment of viral titre and/or viral component abundance.

A metrology system is disclosed. In one embodiment, the metrology system includes a controller communicatively coupled to a reference metrology tool and an optical metrology tool, the controller including one or more processors configured to: generate a geometric model for determining a profile of a test HAR structure from metrology data from a reference metrology tool; generate a material model for determining one or more material parameters of a test HAR structure from metrology data from the optical metrology tool; form a composite model from the geometric model and the material model; measure at least one additional test HAR structure with the optical metrology tool; and determine a profile of the at least one additional test HAR structure based on the composite model and metrology data from the optical metrology tool associated with the at least one HAR test structure.

A laser soldering system and a method for monitoring a laser soldering process by means of a monitoring device of the laser soldering system, wherein a solder ball is dispensed onto a solderable surface of a substrate by means of a solder ball feeding device of the laser soldering system, wherein the solder ball is at least partially melted by means of a laser device of the laser soldering system, wherein, during the laser soldering process, a light signal is formed which is detected by means of an optical detection unit of the monitoring device, wherein the light signal is dispersed into a spectrum of the light signal by means of a spectroscope device of the monitoring device, wherein the spectrum is analyzed by means of a processing device of the monitoring device, and it is identified on the basis of a composition of the spectrum whether or not a burning of the substrate has occurred during the laser soldering process.

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.

Modular docking station and methods for sampling and monitoring gas and other fluids, where a sampling device is able to be removably attached to the docking station, thereby allowing the sampling device to be replaced without having to remove or disconnect the docking station from the rest of the sampling system. This allows the docking station to remain connected to the rest of the system with minimal or no interruption and reduces maintenance costs and time when replacing the sampling device.

A method of defect discovery can include providing a nuisance bin in a nuisance filter, partitioning the defect population into a defect population partition, segmenting the defect population partition into a defect population segment, selecting from the defect population segment a selected set of defects, computing one or more statistics of the signal attributes of the defects in the defect population segment, replicating the selected set of defects to yield generated defects, shifting the generated defects outside of the defect population segment, creating a training set, and training a binary classifier. This method can be operated on a system. The method can enable a semiconductor manufacturer to determine more accurately the presence of defects that would otherwise have gone unnoticed.

In-situ fluorimeters and methods and systems for collecting and analyzing sensor data to predict water source contamination are provided. In one embodiment, a method is provided that includes receiving sensor data regarding a water source. Changepoints may then be calculated within the sensor data and the sensor data may be split into intervals at the changepoints. A machine learning model may then be used to classify the intervals and a predicted contamination event for the water source may be identified based on the classified intervals. In another embodiment, an in-situ fluorimeter is provided. The in-situ fluorimeter comprises one or more UV LEDs centered around a pre-set excitation wavelength (e.g., a TLF excitation wavelength), a bandpass filter, a lens, a photodiode system, a machine learning platform; and an alarm triggered by contamination events, wherein the alarm is calibrated through the machine learning system.

An apparatus and methods of inspecting, analyzing, classifying, and/or grading quality of a transparent sheet using a data set of photoelasticity measurements, thickness measurements, segmentation specifications, measurement specifications and quality control specifications of the transparent sheet. A results measurement is calculated on a computing system, quality control specifications are applied to the results measurement allowing writing to the database and creating reports, sending results to an operator interface and machine control.

According to the embodiment, a processing apparatus includes an arithmetic section. The arithmetic section is configured to calculate a refractive index distribution forming a light beam path based on an estimated output calculated by inputting light beam data indicating the light beam path to an estimation model, an updated output calculated based on the light beam data and the estimated output, and an evaluation index of the estimation model calculated from a ray equation independent of a time which the light beam path follows.

Particulate sensing systems or processes identify particulates suspended in an air sample by irradiating the air sample with UV light and measuring light from individual particles in the air sample. Two photodiodes having different wavelength sensitivity may be used to measure the fluorescent light emitted from a single particle, and a type of the particle may be identified using outputs from photodiodes. Repeating the process for multiple particles may produces distributions that further distinguish or identify particulate types.