Abstract: A radiation detection device includes a plurality of field effect transistors (FETs) arranged to form a resonant cavity. The cavity includes a first end and a second end. The plurality of FETs provide an electromagnetic field defining an standing wave oscillating at a resonant frequency defined by a characteristic of the cavity. A radiation input passing through the cavity induces a perturbation of the electromagnetic field.

Abstract: A radiation detection device includes a plurality of field effect transistors (FETs) arranged to form a resonant cavity. The cavity includes a first end and a second end. The plurality of FETs provide an electromagnetic field defining an standing wave oscillating at a resonant frequency defined by a characteristic of the cavity. A radiation input passing through the cavity induces a perturbation of the electromagnetic field.

Abstract: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.

Abstract: Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables.

Abstract: The profile of a structure having a region with a spatially varying property is modeled using an optical metrology model. A set of profile parameters is defined for the optical metrology model to characterize the profile of the structure. A set of layers is defined for a portion the optical metrology model that corresponds to the region of the structure with the spatially varying property, each layer having a defined height and width. For each layer, a mathematic function that varies across at least a portion of the width of the layer is defined to characterize the spatially varying property within a corresponding layer in the region of the structure.

Abstract: A profile model for use in optical metrology of structures in a wafer is selected, the profile model having a set of geometric parameters associated with the dimensions of the structure. The set of geometric parameters is selected to a set of optimization parameters. The number of optimization parameters within the set of optimization parameters is less than the number of geometric parameters within the set of geometric parameters. A set of selected optimization parameters is selected from the set of optimization parameters. The parameters of the set of selected geometric parameters are used as parameters of the selected profile model. The selected profile model is tested against one or more termination criteria.

Abstract: Specific wavelengths to use in optical metrology of an integrated circuit can be selected using one or more selection criteria and termination criteria. Wavelengths are selected using the selection criteria, and the selection of wavelengths is iterated until the termination criteria are met.

Abstract: To determine one or more features of an in-die structure on a semiconductor wafer, a correlation is determined between one or more features of a test structure to be formed on a test pad and one or more features of a corresponding in-die structure. A measured diffraction signal measured off the test structure is obtained. One or more features of the test structure are determined using the measured diffraction signal. The one or more features of the in-die structure are determined based on the one or more determined features of the test structure and the determined correlation.

Abstract: Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables.

Abstract: An optical metrology model for a structure to be formed on a wafer is developed by characterizing a top-view profile and a cross-sectional view profile of the structure using profile parameters. The profile parameters of the top-view profile and the cross-sectional view profile are integrated together into the optical metrology model. The profile parameters of the optical metrology model are saved.

Abstract: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured from the structure using an optical metrology device. A first profile is obtained from a first machine learning system using the first diffraction signal obtained as an input to the first machine learning system. The first machine learning system is configured to generate a profile as an output for a diffraction signal received as an input. A second profile is obtained from a second machine learning system using the first profile obtained from the first machine learning system as an input to the second machine learning system. The second machine learning system is configured to generate a diffraction signal as an output for a profile received as an input. The first and second profiles include one or more parameters that characterize one or more features of the structure.

Abstract: An optical metrology system includes a photometric device with a source configured to generate and direct light onto a structure, and a detector configured to detect light diffracted from the structure and to convert the detected light into a measured diffraction signal. A processing module of the optical metrology system is configured to receive the measured diffraction signal from the detector to analyze the structure. The optical metrology system also includes a generic interface disposed between the photometric device and the processing module. The generic interface is configured to provide the measured diffraction signal to the processing module using a standard set of signal parameters. The standard set of signal parameters includes a reflectance parameter, a first polarization parameter, a second polarization parameter, and a third polarization parameter.

Abstract: A patterned structure in a wafer is created using one or more fabrication treatment processes. The patterned structure has a treated and an untreated portion. One or more diffraction sensitivity enhancement techniques are applied to the structure, the one or more diffraction sensitivity enhancement techniques adjusting one or more properties of the patterned structure to enhance diffraction contrast between the treated portion and untreated portions. A first diffraction signal is measured off an unpatterned structure on the wafer using an optical metrology device. A second diffraction signal is measured off the patterned structure on the wafer using the optical metrology device. One or more diffraction sensitivity enhancement techniques are selected based on comparisons of the first and second diffraction signals.

Abstract: The profile of a single feature formed on a wafer can be determined by obtaining an optical signature of the single feature using a beam of light focused on the single feature. The obtained optical signature can then be compared to a set of simulated optical signatures, where each simulated optical signature corresponds to a hypothetical profile of the single feature and is modeled based on the hypothetical profile.

Abstract: A correlation between develop inspect (DI) and final inspect (FI) profile parameters are established empirically with test wafers. During production, a wafer is measured at DI phase to obtain DI profile parameters and FI phase profile parameters are predicted according to the DI profile parameters and the established correlation. Each wafer is subsequently measured at FI phase to obtain actual FI profile parameters and the correlation is updated with actual DI and FI profile parameters.

Abstract: A method of generating a library of simulated-diffraction signals (simulated signals) of a periodic grating includes obtaining a measured-diffraction signal (measured signal). Hypothetical parameters are associated with a hypothetical profile. The hypothetical parameters are varied within a range to generate a set of hypothetical profiles. The range to vary the hypothetical parameters is adjusted based on the measured signal. A set of simulated signals is generated from the set of hypothetical profiles.

Abstract: The number of diffraction orders to use in generating simulated diffraction signals for a two-dimensional structure in optical metrology is selected by generating a first simulated diffraction signal using a first number of diffraction orders and a hypothetical profile of the two-dimensional structure. A second simulated diffraction signal is generated using a second number of diffraction orders using the same hypothetical profile used to generate the first simulated diffraction signal, where the first and second numbers of diffraction orders are different. The first and second simulated diffraction signals are compared. Based on the comparison of the first and second simulated diffraction signals, a determination is made as to whether to select the first or second number of diffraction orders.

Abstract: One or more simulated diffraction signals for use in determining the profile of a structure formed on a semiconductor wafer can be generated, where the profile varies in more than one dimension. Intermediate calculations are generated for variations in a hypothetical profile of the structure in a first dimension and a second dimension, where each intermediate calculation corresponds to a portion of the hypothetical profile of the structure. The generated intermediate calculations are then stored and used in generating one or more simulated diffraction signals for one or more hypothetical profiles of the structure.

Abstract: Split machine learning systems can be used to generate an output for an input. When the input is received, a determination is made as to whether the input is within a first, second, or third range of values. If the input is within the first range, the output is generated using a first machine learning system. If the input is within the second range, the output is generated using a second machine learning system. If the input is within the third range, the output is generated using the first and second machine learning systems.

Abstract: A structure formed on a semiconductor wafer is examined by obtaining measurements of cross polarization components of diffraction beams, which were obtained from scanning an incident beam over a range of azimuth angles to obtain an azimuthal scan. A zero azimuth position is determined based on the azimuthal scan. The cross polarization components are zero at the zero azimuth position. A measured diffraction signal is obtained using an azimuth angle to be used in optical metrology of the structure. Misalignment of the azimuth angle is detected using the measured diffraction signal and the determined zero azimuth position.