Methods and systems for feed-forward of multi-layer and multi-process information using XPS and XRF technologies are disclosed. In an example, a method of thin film characterization includes measuring first XPS and XRF intensity signals for a sample having a first layer above a substrate. The first XPS and XRF intensity signals include information for the first layer and for the substrate. The method also involves determining a thickness of the first layer based on the first XPS and XRF intensity signals. The method also involves combining the information for the first layer and for the substrate to estimate an effective substrate. The method also involves measuring second XPS and XRF intensity signals for a sample having a second layer above the first layer above the substrate. The second XPS and XRF intensity signals include information for the second layer, for the first layer and for the substrate. The method also involves determining a thickness of the second layer based on the second XPS and XRF intensity signals, the thickness accounting for the effective substrate.

Methods and systems for feed-forward of multi-layer and multi-process information using XPS and XRF technologies are disclosed. In an example, a method of thin film characterization includes measuring first XPS and XRF intensity signals for a sample having a first layer above a substrate. The first XPS and XRF intensity signals include information for the first layer and for the substrate. The method also involves determining a thickness of the first layer based on the first XPS and XRF intensity signals. The method also involves combining the information for the first layer and for the substrate to estimate an effective substrate. The method also involves measuring second XPS and XRF intensity signals for a sample having a second layer above the first layer above the substrate. The second XPS and XRF intensity signals include information for the second layer, for the first layer and for the substrate. The method also involves determining a thickness of the second layer based on the second XPS and XRF intensity signals, the thickness accounting for the effective substrate.

A method of calculating a thickness of a graphene layer and a method of measuring a content of silicon carbide, by using X-ray photoelectron spectroscopy (XPS), are provided. The method of calculating the thickness of the graphene layer, which is directly grown on a silicon substrate, includes measuring the thickness of the graphene layer directly grown on the silicon substrate, by using a ratio between a signal intensity of a photoelectron beam emitted from the graphene layer and a signal intensity of a photoelectron beam emitted from the silicon substrate.

Provided is a positive electrode active material for a lithium ion secondary battery having favorable cycle characteristics and high capacity. A covering layer containing aluminum and a covering layer containing magnesium are provided on a superficial portion of the positive electrode active material. The covering layer containing magnesium exists in a region closer to a particle surface than the covering layer containing aluminum is. The covering layer containing aluminum can be formed by a sol-gel method using an aluminum alkoxide. The covering layer containing magnesium can be formed as follows: magnesium and fluorine are mixed as a starting material and then subjected to heating after the sol-gel step, so that magnesium is segregated.

XPS spectra are used to analyze and monitor various steps in the selective deposition process. A goodness of passivation value is derived to analyze and quantify the quality of the passivation step. A selectivity figure of merit value is derived to analyze and quantify the selectivity of the deposition process, especially for selective deposition in the presence of passivation. A ratio of the selectivity figure of merit to maximum selectivity value can also be used to characterize and monitor the deposition process.

XPS spectra are used to analyze and monitor various steps in the selective deposition process. A goodness of passivation value is derived to analyze and quantify the quality of the passivation step. A selectivity figure of merit value is derived to analyze and quantify the selectivity of the deposition process, especially for selective deposition in the presence of passivation. A ratio of the selectivity figure of merit to maximum selectivity value can also be used to characterize and monitor the deposition process.

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.

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.

Quantification of the passivation and the selectivity in deposition process is disclosed. The passivation is evaluated by calculating film thicknesses on pattern lines and spaces. An XPS signal is used, which is normalized with X-ray flux number. The method is efficient for calculating thickness in selective deposition process, wherein the thickness can be used as metric to measure selectivity. Measured photoelectrons for each of the materials can be expressed as a function of the thickness of the material overlaying it, adjusted by material constant and effective attenuation length. In selective deposition over a patterned wafer, the three expressions can be solved to determine the thickness of the intended deposition and the thickness of any unintended deposition over passivated pattern.

Quantification of the passivation and the selectivity in deposition process is disclosed. The passivation is evaluated by calculating film thicknesses on pattern lines and spaces. An XPS signal is used, which is normalized with X-ray flux number. The method is efficient for calculating thickness in selective deposition process, wherein the thickness can be used as metric to measure selectivity. Measured photoelectrons for each of the materials can be expressed as a function of the thickness of the material overlaying it, adjusted by material constant and effective attenuation length. In selective deposition over a patterned wafer, the three expressions can be solved to determine the thickness of the intended deposition and the thickness of any unintended deposition over passivated pattern.