Abstract: Joint noise and echo suppression may be performed for enhancing two-way audio communications. Audio data is captured at a communication device and audio data transmitted to the communication device from another communication device are used as input features to a trained machine learning model that uses the transmitted audio data as a reference signal to eliminate residual echo in the captured audio data when also suppressing noise in the captured audio data.

Abstract: Sensors and methods for determining the state of charge of a battery are described. The state of charge is determined in some instances by applying a current perturbation having a frequency to the battery terminals, monitoring the response signal, and determining the phase of the response signal. The phase may be correlated to the state of charge of the battery, so that once the phase is determined, a determination of the state of charge of the battery may be made. In some situations, the state of charge may be used to determine the operating condition of a load connected to the battery. In some embodiments, the state of charge may be used to determine whether the battery is defective.

Abstract: The battery monitoring techniques described herein may be used for detection of battery anomalies or faults. Also, the battery monitoring techniques described herein may be used to generate estimates of total battery capacity. The battery monitoring techniques may employ an alternating current frequency response (ACFR) of the battery. The ACFR response of the battery may be used to detect anomalies and/or estimate a total capacity of the battery.

Abstract: Sensors and methods for determining the state of charge of a battery are described. The state of charge is determined in some instances by applying a current perturbation having a frequency to the battery terminals, monitoring the response signal, and determining the phase of the response signal. The phase may be correlated to the state of charge of the battery, so that once the phase is determined, a determination of the state of charge of the battery may be made. In some situations, the state of charge may be used to determine the operating condition of a load connected to the battery. In some embodiments, the state of charge may be used to determine whether the battery is defective.

Abstract: A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.

Abstract: An inspection system may receive inspection datasets from a defect inspection system associated with inspection of one or more samples, where an inspection dataset of the plurality of inspection datasets associated with a defect includes values of two or more signal attributes and values of one or more context attributes. An inspection system may further label each of the inspection datasets with a class label based on respective positions of each of the inspection datasets in a signal space defined by the two or more signal attributes, where each class label corresponds to a region of the signal space. An inspection system may further segment the inspection datasets into two or more defect groups by training a classifier with the values of the context attributes and corresponding class labels for the inspection datasets, where the two or more defect groups are identified based on the trained classifier.

Abstract: One embodiment is a system for estimating an internal temperature of a battery including a first circuit for receiving a system input signal comprising a measurement of at least one observable quantity associated with the battery and outputting an average battery temperature signal based on the system input signal; and an estimator for receiving the system input signal and the average battery temperature signal and estimating an internal temperature of the battery based on the received signals, wherein the estimator comprises a lumped thermal model of the battery comprising a plurality of parameters.

Abstract: The battery monitoring techniques described herein may be used for detection of battery anomalies or faults. Also, the battery monitoring techniques described herein may be used to generate estimates of total battery capacity. The battery monitoring techniques may employ an alternating current frequency response (ACFR) of the battery. The ACFR response of the battery may be used to detect anomalies and/or estimate a total capacity of the battery.

Abstract: Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.

Abstract: Sensors and methods for determining the state of charge of a battery are described. The state of charge is determined in some instances by applying a current perturbation having a frequency to the battery terminals, monitoring the response signal, and determining the phase of the response signal. The phase may be correlated to the state of charge of the battery, so that once the phase is determined, a determination of the state of charge of the battery may be made. In some situations, the state of charge may be used to determine the operating condition of a load connected to the battery. In some embodiments, the state of charge may be used to determine whether the battery is defective.

Abstract: Systems and methods for detecting defect candidates on a specimen are provided. One method includes, after scanning of at least a majority of a specimen is completed, applying one or more segmentation methods to at least a substantial portion of output generated during the scanning thereby generating two or more segments of the output. The method also includes separately detecting outliers in the two or more segments of the output. In addition, the method includes detecting defect candidates on the specimen by applying one or more predetermined criteria to results of the separately detecting to thereby designate a portion of the detected outliers as the defect candidates.

Abstract: A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.

Abstract: Defects of interest can be captured by a classifier. Images of a semiconductor wafer can be received at a deep learning classification module. These images can be sorted into soft decisions with the deep learning classification module. A class of the defect of interest for an image can be determined from the soft decisions. The deep learning classification module can be in electronic communication with an optical inspection system or other types of semiconductor inspection systems.

Abstract: Methods and systems for setting up inspection of a specimen with design and noise based care areas are provided. One system includes one or more computer subsystems configured for generating a design-based care area for a specimen. The computer subsystem(s) are also configured for determining one or more output attributes for multiple instances of the care area on the specimen, and the one or more output attributes are determined from output generated by an output acquisition subsystem for the multiple instances. The computer subsystem(s) are further configured for separating the multiple instances of the care area on the specimen into different care area sub-groups such that the different care area sub-groups have statistically different values of the output attribute(s) and selecting a parameter of an inspection recipe for the specimen based on the different care area sub-groups.

Abstract: Systems and methods for detecting defect candidates on a specimen are provided. One method includes, after scanning of at least a majority of a specimen is completed, applying one or more segmentation methods to at least a substantial portion of output generated during the scanning thereby generating two or more segments of the output. The method also includes separately detecting outliers in the two or more segments of the output. In addition, the method includes detecting defect candidates on the specimen by applying one or more predetermined criteria to results of the separately detecting to thereby designate a portion of the detected outliers as the defect candidates.

Abstract: An inspection system may receive inspection datasets from a defect inspection system associated with inspection of one or more samples, where an inspection dataset of the plurality of inspection datasets associated with a defect includes values of two or more signal attributes and values of one or more context attributes. An inspection system may further label each of the inspection datasets with a class label based on respective positions of each of the inspection datasets in a signal space defined by the two or more signal attributes, where each class label corresponds to a region of the signal space. An inspection system may further segment the inspection datasets into two or more defect groups by training a classifier with the values of the context attributes and corresponding class labels for the inspection datasets, where the two or more defect groups are identified based on the trained classifier.

Abstract: Methods and systems for setting up inspection of a specimen with design and noise based care areas are provided. One system includes one or more computer subsystems configured for generating a design-based care area for a specimen. The computer subsystem(s) are also configured for determining one or more output attributes for multiple instances of the care area on the specimen, and the one or more output attributes are determined from output generated by an output acquisition subsystem for the multiple instances. The computer subsystem(s) are further configured for separating the multiple instances of the care area on the specimen into different care area sub-groups such that the different care area sub-groups have statistically different values of the output attribute(s) and selecting a parameter of an inspection recipe for the specimen based on the different care area sub-groups.

Abstract: Methods and systems for training an inspection-related algorithm are provided. One system includes one or more computer subsystems configured for performing an initial training of an inspection-related algorithm with a labeled set of defects thereby generating an initial version of the inspection-related algorithm and applying the initial version of the inspection-related algorithm to an unlabeled set of defects. The computer subsystem(s) are also configured for altering the labeled set of defects based on results of the applying. The computer subsystem(s) may then iteratively re-train the inspection-related algorithm and alter the labeled set of defects until one or more differences between results produced by a most recent version and a previous version of the algorithm meet one or more criteria. When the one or more differences meet the one or more criteria, the most recent version of the inspection-related algorithm is outputted as the trained algorithm.

Abstract: Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.

Abstract: Methods and systems for training an inspection-related algorithm are provided. One system includes one or more computer subsystems configured for performing an initial training of an inspection-related algorithm with a labeled set of defects thereby generating an initial version of the inspection-related algorithm and applying the initial version of the inspection-related algorithm to an unlabeled set of defects. The computer subsystem(s) are also configured for altering the labeled set of defects based on results of the applying. The computer subsystem(s) may then iteratively re-train the inspection-related algorithm and alter the labeled set of defects until one or more differences between results produced by a most recent version and a previous version of the algorithm meet one or more criteria. When the one or more differences meet the one or more criteria, the most recent version of the inspection-related algorithm is outputted as the trained algorithm.