Abstract: A method for forecasting remaining useful life (RUL) of a battery by an electronic device is provided. The method includes forecasting the RUL of the battery based on at least one capacity value of the battery estimated by at least one of a battery capacity estimation model and a data driven model, determining whether the at least one capacity value for the charging cycle and the discharging cycle is lower than the at least one capacity value estimated by at least one of the battery capacity estimation model and the data driven model and correcting the forecasting RUL of the battery by feeding back the at least one capacity value to at least one of the battery capacity estimation model and the data driven model.

Abstract: A method for on-device real-time customization of charge profiles for a battery in an electronic device, and/or a corresponding device. The method may include determining at least one charging behavior parameter of the battery during every charging cycle. Further, the method may include determining at least one discharging behavior parameter of the battery subsequent to every charging cycle. Further, the method may include generating a charging profile for charging the battery based on the at least one charging behavior parameter and the at least one discharging behavior parameter. Further, the method may include charging the battery using the generated charging profile for the subsequent charging-discharging cycles.

Abstract: A method, for intelligent management of a battery is provided. The method includes detecting at least one anomaly associated with the battery. The at least one anomaly impacts one or more operations of the battery. The method includes identifying at least one portion of data from reference charging data to include the at least one anomaly for managing the one or more operations of the battery. The method further includes modifying at least one portion of data from the reference charging data based on a pre-determined logic to include the at least one anomaly. The method also includes retraining an Artificial Intelligence (AI) model based on the reference charging data upon modification for managing the one or more operations of the battery.

Abstract: A method for managing a battery includes obtaining a first plurality of battery parameters with respect to a first charging or discharging cycle of the battery; obtaining a second plurality of battery parameters with respect to a second charging or discharging cycle of the battery; determining a relative entropy by comparing the first plurality of battery parameters measured during the first charging or discharging cycle and the second plurality of battery parameters measured during the second charging or discharging cycle; and estimating a relative entropy value to predict a number of cycles after which a battery capacity is predicted to drop.

Abstract: An electronic device, including a memory; a processor; and a remaining useful life (RUL) prediction controller configured to: identify at least one parameter corresponding to at least one of a physical composition and a chemical composition of a plurality of used batteries during at least one of a charging and a discharging of the plurality of used batteries; determine a pattern of variations in at least one of a voltage, a current, a temperature, and a resistance during every cycle of the charging and the discharging of the plurality of used batteries until a failure; generate an artificial intelligence (AI) model which is trained based on a correlation between the determined pattern of variations and the at least one of the physical composition and the chemical composition; and evaluate a RUL of the plurality of used batteries using the AI model.

Abstract: Provided are a battery state measuring method and battery management system, which predict a time point when charging capacity of a battery is to be relatively abruptly reduced. The battery state measuring method includes: monitoring a change of at least one precursor related to the charging capacity of the battery with respect to a number of charging cycles undergone by the battery; and predicting that an abrupt reduction in the charging capacity of the battery is imminent when the change of the at least one precursor follows at least one pre-configured pattern of the battery that has undergone a critical number of charging cycles.

Abstract: Provided is a method for detecting at least one anomaly in a battery, the method including obtaining, by a processor, charging-discharging data of the battery that has undergone a preset number of charging-discharging cycles, and obtaining, by the processor, a probability of the battery being healthy and at least one probability of the battery having an anomaly of at least one class, based on a correlation between charging-discharging data of a plurality of reference batteries and the charging-discharging data of the battery.

Abstract: In a non-limiting embodiment, a magnetic memory device includes a memory component having a plurality of magnetic storage elements for storing memory data, and one or more sensor components configured to detect a magnetic field external to the memory component. The sensor component outputs a signal to one or more components of the magnetic memory device based on the detected magnetic field. The memory component is configured to be terminated when the signal is above a predetermined threshold value. In some embodiments, a magnetic field is generated in a direction opposite to the direction of the detected external magnetic field when the signal is above the predetermined threshold value.

Abstract: Provided are a battery state measuring method and battery management system, which predict a time point when charging capacity of a battery is to be relatively abruptly reduced. The battery state measuring method includes: monitoring a change of at least one precursor related to the charging capacity of the battery with respect to a number of charging cycles undergone by the battery; and predicting that an abrupt reduction in the charging capacity of the battery is imminent when the change of the at least one precursor follows at least one pre-configured pattern of the battery that has undergone a critical number of charging cycles.

Abstract: In a non-limiting embodiment, a magnetic memory device includes a memory component having a plurality of magnetic storage elements for storing memory data, and one or more sensor components configured to detect a magnetic field external to the memory component. The sensor component outputs a signal to one or more components of the magnetic memory device based on the detected magnetic field. The memory component is configured to be terminated when the signal is above a predetermined threshold value. In some embodiments, a magnetic field is generated in a direction opposite to the direction of the detected external magnetic field when the signal is above the predetermined threshold value.

Abstract: Structures for a sensor and methods of forming such structures. A sensing element includes a free magnetic layer, a pinned magnetic layer, and a non-magnetic conductive spacer layer between the free magnetic layer and the pinned magnetic layer. A dummy element is positioned outside of an outer boundary of the sensing element. The dummy element is detached from the sensing element.

Abstract: In a non-limiting embodiment, a magnetic memory device includes a memory component having a plurality of magnetic storage elements for storing memory data, and one or more sensor components configured to detect a magnetic field external to the memory component. The sensor component outputs a signal to one or more components of the magnetic memory device based on the detected magnetic field. The memory component is configured to be terminated when the signal is above a predetermined threshold value. In some embodiments, a magnetic field is generated in a direction opposite to the direction of the detected external magnetic field when the signal is above the predetermined threshold value.

Abstract: In a non-limiting embodiment, a magnetic memory device includes a memory component having a plurality of magnetic storage elements for storing memory data, and one or more sensor components configured to detect a magnetic field external to the memory component. The sensor component outputs a signal to one or more components of the magnetic memory device based on the detected magnetic field. The memory component is configured to be terminated when the signal is above a predetermined threshold value. In some embodiments, a magnetic field is generated in a direction opposite to the direction of the detected external magnetic field when the signal is above the predetermined threshold value.

Abstract: In one embodiment, the invention comprises: defining a first volume in a layer of a semiconductor device; calculating a probability of finding at least one dopant atom in the first volume, based on a dopant distribution of the layer; in the case that the calculated probability is equal to or greater than a pre-determined threshold, defining at least one additional volume in the layer substantially equal to the first volume; and in the case that the calculated probability is less than the pre-determined threshold: aggregating the first volume with a second volume adjacent the first volume, the second volume being substantially equal to the first volume; and recalculating a probability of finding at least one dopant atom in the aggregated first and second volumes, based on the dopant distribution of the layer.

Abstract: Various embodiments each include at least one of systems, devices, methods, and software for chatbot order submission by group members for products to an entity that will provide the ordered products. The group may be just a single person or may be many people. One example embodiment that may be performed by a chatbot or an element of a chat host platform includes identifying an activity command received over a network from a participant within a text-based chat session. This method further includes executing a data processing activity associated with the identified activity command, the executing of the activity including generating and transmitting task data over the network to a data processing system of an entity that will perform a task based on the task data.

Abstract: In one embodiment, the invention comprises: defining a first volume in a layer of a semiconductor device; calculating a probability of finding at least one dopant atom in the first volume, based on a dopant distribution of the layer; in the case that the calculated probability is equal to or greater than a pre-determined threshold, defining at least one additional volume in the layer substantially equal to the first volume; and in the case that the calculated probability is less than the pre-determined threshold: aggregating the first volume with a second volume adjacent the first volume, the second volume being substantially equal to the first volume; and recalculating a probability of finding at least one dopant atom in the aggregated first and second volumes, based on the dopant distribution of the layer.

Abstract: A method is provided for modeling charge distribution on FinFET sidewalls for estimating variability in device performance. The method includes: inputting structure parameters and simulation parameters for a FinFET structure; identifying a semiconductor-oxide interface in the structure, the interface including a plurality of atomic steps and a plurality of trapped charges; distributing charges at the interface; and performing device simulations and current-voltage analysis upon generating all samples of given number of devices.

Abstract: Illustrative embodiments of a vertical tunneling field effect transistor are disclosed which may comprise a semiconductor body including a source region doped with a first dopant type and a pocket region doped with a second dopant type, where the pocket region is formed above the source region. The transistor may also comprise an insulated gate formed above the source and pocket regions, the insulated gate being configured to generate electron tunneling between the source and pocket regions if a voltage is applied to the insulated gate. The transistor may further comprise a lateral tunneling barrier formed to substantially prevent electron tunneling between the source region and a drain region of the semiconductor body, where the drain region is doped with the second dopant type.

Abstract: Illustrative embodiments of a vertical tunneling field effect transistor are disclosed which may comprise a semiconductor body including a source region doped with a first dopant type and a pocket region doped with a second dopant type, where the pocket region is formed above the source region. The transistor may also comprise an insulated gate formed above the source and pocket regions, the insulated gate being configured to generate electron tunneling between the source and pocket regions if a voltage is applied to the insulated gate. The transistor may further comprise a lateral tunneling barrier formed to substantially prevent electron tunneling between the source region and a drain region of the semiconductor body, where the drain region is doped with the second dopant type.