Samsung Electronics Co., Ltd.

Globalfoundries Singapore Pte. Ltd.

International Business Machines Corporation

Globalfoundries Inc.

Samarth Agarwal

Eng Huat Toh

Krishnan Seethalakshmi Hariharan

Ruchil Kumar Jain

Seongho Han

Bin Liu

Kiok Boone Elgin Quek

Subramanian Swernath Brahmadathan

Terence B Hook

Shyue Seng Tan

Jeffrey Bowman Johnson

Mohit Bajaj

Lanxiang Wang

Ping Zheng

Anshul Kaushik

Rajkumar Subhash Patil

Achyutha Krishna Koneti

Deekshith Pathayappilly Krishnan

Abhisek Dixit

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.

Methods and systems for detecting faulty behavior in a battery

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.

Method and electronic device for managing a battery

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.

Battery state measuring method and battery management system

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.

Magnetic memory devices with magnetic field sensing and shielding

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.

Sensors including dummy elements arranged about a sensing element

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.

Modeling random dopant fluctuations in semiconductor devices

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.

Modeling charge distribution on FinFET sidewalls

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Bengaluru, India

Samarth Agarwal