Abstract: Provided is a method for virtually executing an operation of an energy dispersive x-ray spectrometry (EDS) system in real time production line by analyzing a defect included in a material undergoing inspection based on computer vision, the method including receiving a scanning electron microscope (SEM) image of the material including the defect, extracting an image-feature from the SEM image of the material, classifying the extracted image-feature under a predetermined label, predicting, based on the classified image-feature, an element associated with the defect included in the material and a shape of the predicted element, and grading the defect included in the material based on comparing the predicted element with a predetermined criteria.

Abstract: Example embodiments may provide methods for determining a quality of a film in spin coating process. The methods may include capturing images of portions of the film using an imaging device while coating the film on a substrate using a spinner. The imaging device may include SPCs and lens and/or SLMs. The methods may also include determining whether a characteristic of the film matches to a standard based on the images of the portions of the film. The method may further include performing detecting that the quality of the film is optimal in response to determining that the characteristic of the film matches to the standard or detecting that the quality of the film is not optimal in response to determining that the characteristic of the film does not match to the standard.

Abstract: Provided is a method for virtually executing an operation of an energy dispersive x-ray spectrometry (EDS) system in real time production line by analyzing a defect included in a material undergoing inspection based on computer vision, the method including receiving a scanning electron microscope (SEM) image of the material including the defect, extracting an image-feature from the SEM image of the material; classifying the extracted image-feature under a predetermined label, predicting, based on the classified image-feature, an element associated with the defect included in the material and a shape of the predicted element, and grading the defect included in the material based on comparing the predicted element with a predetermined criteria.

Abstract: Systems, methods, and other embodiments associated with computing scenario forecasts according to electronic inputs are described. In one embodiment, a method includes, in response to receiving a signal that triggers data collection, collecting electronic data from one or more electronic databases by aggregating the electronic data into data structures of a processing table. The electronic data defines historic values of a set of instruments. The method also includes computing projected values for each of the set of instruments according to correlations identified in the historic values. The projected values form primary forecasts that model expected future values of the set of instruments. The method includes, in response to receiving electronic inputs including scenario variables of a scenario that affects the primary forecasts, generating scenario forecasts for the set of instruments according to the scenario variables and the projected values to identify how the scenario influences the primary forecasts.

Abstract: Systems, methods, and other embodiments associated with computing scenario forecasts according to electronic inputs are described. In one embodiment, a method includes, in response to receiving a signal that triggers data collection, collecting electronic data from one or more electronic databases by aggregating the electronic data into data structures of a processing table. The electronic data defines historic values of a set of instruments. The method also includes computing projected values for each of the set of instruments according to correlations identified in the historic values. The projected values form primary forecasts that model expected future values of the set of instruments. The method includes, in response to receiving electronic inputs including scenario variables of a scenario that affects the primary forecasts, generating scenario forecasts for the set of instruments according to the scenario variables and the projected values to identify how the scenario influences the primary forecasts.