Abstract: A method for predicting quality of a workpiece includes: obtaining optical emission spectroscopy (OES) raw data of the workpiece, the OES raw data having a time dimension and a wavelength dimension; selecting, in the wavelength dimension, a first portion of the OES raw data that falls within wavelength ranges selected based on a material used in a process of producing the workpiece; selecting, in the time dimension, a second port of the OES raw data that falls within a time range corresponding to a specific step of the process, where the specific step is to be analyzed; generating tokens by grouping the selected portions of the OES raw data; and training a model with the tokens.

Abstract: A method including generating sequence data based on measured values of respective one or more process factors of each of a plurality of processes of a semiconductor fabrication process for a wafer within the semiconductor fabrication process, generating a temporary quality index of the wafer using a second neural network connected to a first neural network that is provided the sequence data, training the first neural network and the second neural network based on a loss between the temporary quality index and a set actual quality index of the wafer, selecting a control process from among the plurality of processes using at least one of the trained first neural network and/or the trained second neural network, and selecting a control factor from among multiple process factors of the selected process using the trained second neural network.

Abstract: A battery state estimation method includes acquiring an initial value of a vector-type parameter for modeling an electrochemical-thermal (ECT) model of a battery, extracting a predetermined point from the vector-type parameter based on the initial value, generating a target parameter based on the predetermined point, to minimize an error between an actual state of the battery and a state of the battery acquired from the ECT model, and estimating the state of the battery based on the target parameter.

Abstract: A battery state estimation method includes acquiring an initial value of a vector-type parameter for modeling an electrochemical-thermal (ECT) model of a battery, extracting a predetermined point from the vector-type parameter based on the initial value, generating a target parameter based on the predetermined point, to minimize an error between an actual state of the battery and a state of the battery acquired from the ECT model, and estimating the state of the battery based on the target parameter.

Abstract: A processor-implemented method of estimating a state of a battery includes acquiring current information and voltage information of a battery; determining time interval values based on the acquired current information such that current integration values corresponding to the time variation values satisfy a condition; determining voltage values corresponding to the determined time interval values in the acquired voltage information; and determining state information of the battery based on the determined voltage values.

Abstract: A processor-implemented method with data exploration includes: setting first input data and a first target condition; predicting first output data corresponding to the first input data using a first function that models an objective function; and determining second input data using a second function that provides a result of comparison between the first output data and the first target condition.

Abstract: A battery state estimation method includes acquiring an initial value of a vector-type parameter for modeling an electrochemical-thermal (ECT) model of a battery, extracting a predetermined point from the vector-type parameter based on the initial value, generating a target parameter based on the predetermined point, to minimize an error between an actual state of the battery and a state of the battery acquired from the ECT model, and estimating the state of the battery based on the target parameter.

Abstract: A method with battery state estimation may include: generating a plurality of electrochemical-thermal (ECT) models corresponding to modeling conditions based on an arbitrary parameter of an ECT model configured to perform modeling of a battery; determining a target parameter of the ECT model based on an error between an actual state of the battery and a model-derived state of the battery obtained by a parallel operation of the plurality of ECT models; and estimating a state of the battery based on the target parameter.

Abstract: A battery state estimation method includes acquiring an initial value of a vector-type parameter for modeling an electrochemical-thermal (ECT) model of a battery, extracting a predetermined point from the vector-type parameter based on the initial value, generating a target parameter based on the predetermined point, to minimize an error between an actual state of the battery and a state of the battery acquired from the ECT model, and estimating the state of the battery based on the target parameter.

Abstract: A method with battery state estimation may include: generating a plurality of electrochemical-thermal (ECT) models corresponding to modeling conditions based on an arbitrary parameter of an ECT model configured to perform modeling of a battery; determining a target parameter of the ECT model based on an error between an actual state of the battery and a model-derived state of the battery obtained by a parallel operation of the plurality of ECT models; and estimating a state of the battery based on the target parameter.

Abstract: Disclosed is a battery state estimation method and apparatus, the method includes extracting data from target intervals in sensing data of a battery, generating feature vectors of the data extracted from each of the target intervals, applying a weight to each of the generated feature vectors, merging the feature vectors to which the weight is applied, and determining state information of the battery based on the merging.

Abstract: A battery state estimation method and apparatus are provided. Sensing data of a battery is received, and feature information is acquired by preprocessing the sensing data. The preprocessed sensing data is selected based on the feature information, and state information of the battery is determined based on at least one of the selected preprocessed sensing data or previous state information of the battery.

Abstract: A processor-implemented method of estimating a state of a battery includes acquiring current information and voltage information of a battery; determining time interval values based on the acquired current information such that current integration values corresponding to the time variation values satisfy a condition; determining voltage values corresponding to the determined time interval values in the acquired voltage information; and determining state information of the battery based on the determined voltage values.

Abstract: A battery state estimation method and apparatus are provided. Sensing data of a battery is received, and feature information is acquired by preprocessing the sensing data. The preprocessed sensing data is selected based on the feature information, and state information of the battery is determined based on at least one of the selected preprocessed sensing data or previous state information of the battery.

Abstract: A battery state estimation method and apparatus are provided. Sensing data of a battery is received, and feature information is acquired by preprocessing the sensing data. The preprocessed sensing data is selected based on the feature information, and state information of the battery is determined based on at least one of the selected preprocessed sensing data or previous state information of the battery.

Abstract: A method of estimating a life of a battery includes acquiring density data based on battery sensing data, determining a density feature based on the density data using clusters generated by clustering a density data set based on a plurality of battery management profiles, and estimating the life of the battery based on the density feature.

Abstract: An apparatus for estimating a state of a battery including a data receiver configured to receive module data associated with a battery module included in a battery pack from the battery module, and a processor configured to acquire cell data corresponding to a battery cell included in the battery module from the module data, and determine a module state of the battery module based on the cell data.

Abstract: A battery state estimation method and apparatus. The apparatus includes a time detector configured to detect a time during which a voltage level of a battery, being partially charged and discharged, changes through a constant current charge. The apparatus also includes a state of health (SoH) information estimator configured to estimate SoH information of the battery with the changed voltage level and the detected time, based on predetermined reference information.

Abstract: Provided is a battery state estimation apparatus and method that determine a validity of a battery model, which is dependent on a parameter, based on state information of a battery that is estimated from battery information of the battery, transmit an update request for the battery model to an external battery model provider in response to a result of the determining indicating that the battery model is invalid, receive another parameter in response to the update request, and update the battery model based on the other parameter.

Abstract: A battery state estimation apparatus and method are provided. The processor implemented battery state estimation apparatus estimates attention data indicating data suitable for a state estimation from battery data, and estimates battery state information based on the battery data and the attention data.