The present disclosure generally provides for a system and method for measuring one or more characteristics of one or more substrates in a multi-station processing system using one or more metrology modules at a plurality of metrology stations. In one embodiment, a system controller is configured to cause the multi-station processing system to perform a method that includes processing a plurality of substrates at a plurality of processing stations, advancing one or more of the plurality of substrates to a respective metrology station, measuring one or more characteristics of the plurality of substrates at the respective metrology station, determining a processing performance metric based on the one or more characteristics, comparing the processing performance metric to a tolerance limit to determine if an out of tolerance condition has occurred, and adjusting one or more processing parameters when it is determined that an out of tolerance condition has occurred.

Some devices, systems, and methods obtain one or more images of a substrate, wherein the substrate includes a feature pattern; determine one or more edges of the feature pattern based on the one or more images; determine an offset of the feature pattern relative to the substrate and an angle of the feature pattern relative to the substrate based on the one or more edges of the feature pattern; and generate a transformation for a drop pattern based on the offset of the feature pattern and on the angle of the feature pattern.

A measuring device for measuring a position of a pattern includes a wavelength variable unit configured to vary a spectrum of first light in accordance with an incident position at which the first light is incident to allow the first light to pass therethrough, and a moving unit configured to change the incident position by moving the wavelength variable unit. The wavelength variable unit is moved to a position based on wavelength characteristic information and intensity characteristic information, the wavelength characteristic information indicating a relationship between the position of the wavelength variable unit and a wavelength of the first light transmitted through the wavelength variable unit, the intensity characteristic information indicating a relationship between the position of the wavelength variable unit and an intensity of second light from the pattern illuminated with the first light transmitted through the wavelength variable unit.

A method of treating a surface of a reticle includes retrieving a reticle from a reticle library and transferring the reticle to a treatment device. The surface of the reticle is treated in the treatment device by irradiating the surface of the reticle UV radiation while ozone fluid is over the surface of the reticle for a predetermined irradiation time. After the treatment, the reticle is transferred to an exposure device for lithography operation to generate a photo resist pattern on a wafer. A surface of the wafer is imaged to generate an image of the photo resist pattern on the wafer. The generated image of the photo resist pattern is analyzed to determine critical dimension uniformity (CDU) of the photo resist pattern. The predetermined irradiation time is increased if the CDU does not satisfy a threshold CDU.

Disclosed herein is a method of measuring a pattern on a substrate comprising: preparing a substrate having a relief pattern comprising organic or inorganic material; directing an excitation light to the relief pattern on the substrate to emit a fluorescent light from the relief pattern; detecting an intensity of the fluorescent light emitted from the relief pattern; and determining a volume of the relief pattern on the substrate based on the detected intensity of the fluorescent light.

Described herein is a method of training a model configured to predict whether a feature associated with an imaged substrate will be defective after etching of the imaged substrate and determining etch conditions based on the trained model. The method includes obtaining, via a metrology tool, (i) an after development image of the imaged substrate at a given location, the after development image including a plurality of features, and (ii) an after etch image of the imaged substrate at the given location; and training, using the after development image and the after etch image, the model configured to determine defectiveness of a given feature of the plurality of features in the after development image. In an embodiment, the determining of defectiveness is based on comparing the given feature in the after development image with a corresponding etch feature in the after etch image.

A method for improving the yield of a lithographic process, the method including: determining a parameter fingerprint of a performance parameter across a substrate, the parameter fingerprint including information relating to uncertainty in the performance parameter; determining a process window fingerprint of the performance parameter across the substrate, the process window being associated with an allowable range of the performance parameter; and determining a probability metric associated with the probability of the performance parameter being outside an allowable range. Optionally a correction to the lithographic process is determined based on the probability metric.

Described herein is a method for training a machine learning model to determine a source of error contribution to multiple features of a pattern printed on a substrate. The method includes obtaining training data having multiple datasets, wherein each dataset has error contribution values representative of an error contribution from one of multiple sources to the features, and wherein each dataset is associated with an actual classification that identifies a source of the error contribution of the corresponding dataset; and training, based on the training data, a machine learning model to predict a classification of a reference dataset of the datasets such that a cost function that determines a difference between the predicted classification and the actual classification of the reference dataset is reduced.

A computer-implemented method for determining optical roughness in a semiconductor pattern structure that includes receiving, using a processor, optical responses spectra collected from the semiconductor pattern structure and constructing, using the processor optical critical dimension (OCD) models by using a set of input parameters for each layer of the semiconductor pattern structure. The method further includes calculating, using the processor, theoretical optical responses from a theoretical input generated by the OCD models. In addition, the method provides for comparing, using the processor, the optical responses spectra of the semiconductor pattern structure to the theoretical optical responses to determine output parameters for the optical roughness of the semiconductor pattern structure.

Disclosed is a detection apparatus for a metrology device operable to measure a parameter of interest from scattered radiation having been scattered from a sample. The detection device comprises a detector comprising an array of pixels. The array of pixels comprises imaging pixels for detecting an image from which the parameter of interest is determined, and direction detecting pixels for detecting the angle of incidence of said scattered radiation on said detector.