A method for analyzing a polymer membrane, which can improve accuracy of structural analysis of the polymer membrane and shorten the analysis time by effectively removing noise is provided.

The disclosure is directed to a method and apparatus for correcting responsivity variation in photothermal imaging. The method includes sending, during a first time period, light-driving signal to a light source so that the light source is configured to output a series of light pulses onto a sample, wherein the sample is under photothermal-induced expansion according to the series of light pulses. The method includes obtaining, during the first time period, first deflection signal from a cantilever. The method includes sending, during a second time period, actuator-driving signal to an electromechanical actuator so that the electromechanical actuator is configured to move according to the actuator-driving signal, wherein the electromechanical actuator is coupled with the sample. The method includes obtaining, during the second time period, second deflection signal from the cantilever and obtaining a photothermal image of the sample based on the first deflection signal and the second deflection signal.

An atomic-force-microscope-based apparatus and method including hardware and software, configured to collect, in a dynamic fashion, and analyze data representing mechanical properties of soft materials on a nanoscale, to map viscoelastic properties of a soft-material sample. The use of the apparatus as an addition to the existing atomic-force microscope device.

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, using the model to predict the low-probability stochastic defect, determining a process window based on the low-probability stochastic defect, and controlling, based on the process window, a lithography tool to manufacture a device.

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, using the model to predict the low-probability stochastic defect, determining a process window based on the low-probability stochastic defect, and controlling, based on the process window, a lithography tool to manufacture a device.

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, calibrating, using unbiased measurement data, the model to a specific lithography process, patterning process, or both to generate a calibrated model, using the calibrated model to predict the low-probability stochastic defect; and modifying, based on the low-probability stochastic defect, a variable, parameter, setting, or some combination of a manufacturing process of a device.

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, calibrating, using unbiased measurement data, the model to a specific lithography process, patterning process, or both to generate a calibrated model, using the calibrated model to predict the low-probability stochastic defect; and modifying, based on the low-probability stochastic defect, a variable, parameter, setting, or some combination of a manufacturing process of a device.

An atomic-force-microscope-based apparatus and method including hardware and software, configured to collect, in a dynamic fashion, and analyze data representing mechanical properties of soft materials on a nanoscale, to map viscoelastic properties of a soft-material sample. The use of the apparatus as an addition to the existing atomic-force microscope device.

Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.

Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.