Abstract: A method for determining values of design variables of a lithographic process based on a predicted failure rate for printing a target pattern on a substrate using a lithographic apparatus. The method includes obtaining an image corresponding to a target pattern to be printed on a substrate using a lithographic apparatus, wherein the image is generated based on a set of values of design variables of the lithographic apparatus or a lithographic process; determining image properties, the image properties representative of a pattern printed on the substrate, the pattern corresponding to the target pattern; predicting a failure rate in printing the pattern on the substrate based on the image properties; and determining a specified value of a specified design variable based on the failure rate, the specified value to be used in the lithographic process to print the target pattern on the substrate.

Abstract: A system uses a machine learning model trained using multitask learning for processing audio signals of different types. The system may be used in a device such as a headset, smart glasses, or a smart watch, and may be used to make predictions based on different types of audio signals, for example, for classifying acoustic events based on audio signal or for keyword spotting to detect wake words. The use of a single machine learning model for analyzing different types of audio signals improves storage efficiency as well as energy efficiency of devices compared to systems that use a different machine learning model for processing each type of audio signal.

Abstract: Methods for configuring a patterning process based on results of another patterning process is described. The method includes obtaining a first set of contours by simulating a first patterning process using a design layout in a first orientation. The contours satisfy a design specification associated with the design layout and correspond to a first set of process window conditions. A second patterning process is configured based on a second orientation of the design layout, the first set of process window conditions and the first set of contours. The second patterning process is associated with one or more design variables (e.g., illumination, mask pattern) that affect a second set of contours. The configuring includes adjusting one or more design variables until the second set of contours are within a desired matching threshold with the first set of contours.

Abstract: A structural stabilizer for a fiber and proppant complex to enhance proppant migration and placement in a fractured well during propping and a preparation method thereof are provided. The structural stabilizer consists of: water, inorganic salt, kaolinite, nitrogen-doped modified graphene oxide, anionic surfactant, non-ionic alkyl polyglucoside, and polyacrylamide. The structural stabilizer improves bonding between a proppant and a fiber when slick water is used in stimulated reservoir volume (SRV) fracturing, prevents separation of the fiber and the proppant during migration, thereby reducing escape rate of the fiber from the fiber and proppant complex.

Abstract: A power tool having one or more sound damping members which reduce sound and/or vibration from one or more parts of a power tool. The sound damping member can reduce sound and/or vibration from static or dynamic parts of a power tool. The sound damping member can reduce noise and/or vibration from one or more rotating or moving parts of a power tool and its housing or internal structure. Methods, means, controls, systems and practices for reducing or eliminating undesired sound from a power tool are disclosed.

Abstract: Aspects of the disclosure provide methods for determining wafer flatness and for fabricating a semiconductor device. The method includes storing a first wafer expansion of a first wafer that is collected along a first direction parallel to a working surface of the first wafer during a lithography process. The lithography process is for patterning structures on the working surface of the first wafer. Before a fabrication step with a wafer flatness requirement, a wafer flatness of the first wafer is determined based on the first wafer expansion collected during the lithography process using a flatness prediction model that is configured to predict the wafer flatness. In an example, a layer is deposited on a back side of the first wafer with a thickness that is based on the determined wafer flatness of the first wafer.

Abstract: Embodiments of the present disclosure disclose a method and an apparatus for restoring a NETCONF server to factory defaults, and relate to the field of configuration management technologies. A NETCONF server receives a command for restoring to factory defaults sent by a NETCONF client, where the command for restoring to factory defaults is a remote procedure call (RPC) command that is based on a Yang model of the NETCONF for restoring the NETCONF server to the factory defaults. The NETCONF server replaces data in the running configuration datastore with factory defaults of the NETCONF server according to the command for restoring to factory defaults.

Abstract: In one embodiment, a system includes an automatic speech recognition (ASR) module, a natural-language understanding (NLU) module, a dialog manager, one or more agents, an arbitrator, a delivery system, one or more processors, and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to receive a user input, process the user input using the ASR module, the NLU module, the dialog manager, one or more of the agents, the arbitrator, and the delivery system, and provide a response to the user input.

Abstract: The present disclosure relates to a computer-implemented method for routing in an electronic design. Embodiments may include receiving, using at least one processor, global route data associated with an electronic design as an input and generating detail route data, based upon, at least in part, the global route data. Embodiments may further include transforming one or more of the detail route data and the global route data into at least one input feature and at least one output result of a deep neural network. Embodiments may also include training the deep neural network with the global route data and the detail route data and predicting an output associated with a detail route based upon, at least in part, a trained deep neural network model.

Abstract: The present disclosure disclose a configuration management method and apparatus, and a device. A control device determines that an application message needs to be published to a target logical group. The control device obtains an identifier of the target logical group and queries, according to a gateway mapping table and the identifier of the target logical group, a gateway connected to each of the at least one target terminal, to obtain at least one target gateway. The control device generates based on the application message, a MQTT publish packet and sends the MQTT publish packet to the at least one target gateway, where the MQTT publish packet is used to instruct a target gateway that receives the MQTT publish packet to send a corresponding application message to each target terminal that is connected to the target gateway and that belongs to the target logical group.

Abstract: User data from a received service processing request initiated by a user for processing a service is parsed. Whether the user data is included in a trusted user list or a risky user list stored in a server for processing the service is determined. Whether the user data matches historical behavior data associated with the user based on a behavioral profile comparison rule model if the user data is not included in the trusted user list or the risky user list is determined. A risk type associated with the user data is determined if the user data fails to match the historical behavior data. A risk identification rule model associated with the risk type is determined based on the service requested. Risk identification is performed based on the user data using the determined risk identification rule model. The service is processed by the server based on a result of the risk identification.

Abstract: A high dynamic range image sensors enabled by integrating broadband optical filters with individual sensor pixels of a pixel array. The broadband optical filters are formed of engineered micro or nanostructures that exhibit large differences in transmittance, e.g. up to 5 to 7 orders of magnitude. Such high transmittance difference can be achieved by using a single layer of individually designed filters, which show varied transmittance as a result of the distinct absorption of various material and structures. The high transmittance difference can also be achieved by controlling the polarization of light and using polarization-sensitive structures as filters. With the presence of properly designed integrated nanostructures, broadband transmission spectrum with transmittance spanning several orders of magnitude can be achieved. This enables design and manufacturing of image sensors with high dynamic range which is crucial for applications including autonomous driving and surveillance.

Abstract: Embodiments of the present disclosure disclose a method and an apparatus for restoring a NETCONF server to factory defaults, and relate to the field of configuration management technologies. A NETCONF server receives a command for restoring to factory defaults sent by a NETCONF client, where the command for restoring to factory defaults is a remote procedure call (RPC) command that is based on a Yang model of the NETCONF for restoring the NETCONF server to the factory defaults. The NETCONF server replaces data in the running configuration datastore with factory defaults of the NETCONF server according to the command for restoring to factory defaults.

Abstract: Implementations of the present disclosure discloses user identity verification method selection method and device. In an implementation, times and corresponding verification methods of successful user identity verifications are recorded, one or more verification methods of successful user identity verifications performed in a predetermined time period based on the recorded times and corresponding verification methods are identified. Whether at least one verification method is included in both the identified one or more verification methods of successful user identity verifications and a list of available verification methods are determined. One or more available verification methods different from the identified one or more verification methods are used for user identity verification if no verification method is included in both the identified one or more verification methods of successful user identity verifications and the list of available verification methods.

Abstract: A high dynamic range image sensors enabled by integrating broadband optical filters with individual sensor pixels of a pixel array. The broadband optical filters are formed of engineered micro or nanostructures that exhibit large differences in transmittance, e.g. up to 5 to 7 orders of magnitude. Such high transmittance difference can be achieved by using a single layer of individually designed filters, which show varied transmittance as a result of the distinct absorption of various material and structures. The high transmittance difference can also be achieved by controlling the polarization of light and using polarization-sensitive structures as filters. With the presence of properly designed integrated nanostructures, broadband transmission spectrum with transmittance spanning several orders of magnitude can be achieved. This enables design and manufacturing of image sensors with high dynamic range which is crucial for applications including autonomous driving and surveillance.

Abstract: Embodiments of the present disclosure disclose a method and an apparatus for restoring a network device to factory defaults, and a network device, and relate to the field of configuration management technologies. A NETCONF server determines that the network device needs to be restored to the factory defaults; the NETCONF server replaces data in a startup configuration datastore and a running configuration datastore in the NETCONF server with factory defaults data in a factory defaults datastore, where the factory defaults datastore is dedicated to storing the factory defaults data of the network device. The NETCONF server delivers the data in the running configuration datastore after replacement to a plurality of service modules. Then, the plurality of service modules performs a validation operation based on the data delivered by the NETCONF server.

Abstract: User data from a received service processing request initiated by a user for processing a service is parsed. Whether the user data is included in a trusted user list or a risky user list stored in a server for processing the service is determined. Whether the user data matches historical behavior data associated with the user based on a behavioral profile comparison rule model if the user data is not included in the trusted user list or the risky user list is determined. A risk type associated with the user data is determined if the user data fails to match the historical behavior data. A risk identification rule model associated with the risk type is determined based on the service requested. Risk identification is performed based on the user data using the determined risk identification rule model. The service is processed by the server based on a result of the risk identification.

Abstract: A spectral sensing system includes an array of sampling optical elements (e.g. filters) and an array of optical sensors, and a deep learning model used to process output of the optical sensor array. The deep learning model is trained using a training dataset which includes, as training inputs, optical sensor output generated by shining each one of multiple different light waves with known spectra on the sampling optical element array for multiple different times, each time from a known angle of incidence, and as training labels, the known spectra of the multiple light waves. The trained deep learning model can be used to process output of the optical sensor array when light having an unknown spectrum is input on the sampling optical element array from unknown angles, to measure the spectrum of the light. The spectral sensing system can also be used to form a hyperspectral imaging system.