Abstract: Manufacturing management information relating to a module battery and single battery mounted therein which are obtained in a battery manufacturing process (1) is collected by a manufacturing quality information collection processing unit (4) and stored in a database (6). In a battery diagnosis system (3), when the module battery (2) under a usage environment is charged, an operation result processing unit (5) collects operation result information relating to the module (2) and stores the operation result information in a database (7). An operation result monitoring processing unit (8) determines whether the operation result information is abnormal, and supplies the result of the determination to a manufacturing/usage environment factor classification processing unit (9).

Abstract: Provided is a technology for assisting quick preparation of a maintenance plan. A maintenance device includes: a maintenance operation identification part configured to acquire, for an alarm received via a network, from another device that has transmitted the alarm via the network, information for identifying the alarm and information output from a sensor causing generation of the alarm, and extract required maintenance operations based on the information for identifying the alarm and the information output from the sensor; a constraint information identification part configured to identify constraint information including a facility required for each of the required maintenance operations; and an optimal operation plan calculation part configured to identify an execution order of the required maintenance operations based on a priority order in the constraint information, and assign predetermined time zones, to thereby generate a maintenance operation plan.

Abstract: Proposed is a method for efficiently indicating the replacement timing of each battery module which is a constituent element of a battery system on the basis of operational results information of the battery system without lowering the operating ratio of the battery system. First, capacity-voltage profile data of the battery module according to manufacturing state and degradation state, capacity-voltage profile data at the time of shipping of the battery module, and most recent capacity-voltage profile data of the battery module are collated to estimate the degradation degree of the battery module of the battery system. Then, based on past charge/discharge results data of the battery module, a future use pattern is estimated. Thereafter, remaining operating life is computed based on the degradation degree, the use pattern of the battery module, and characteristics degradation data.

Abstract: A technique relating to an apparatus abnormality diagnosis system, capable of easily creating and adding/updating an diagnosis model with respect to an initial and new failure case, and appropriately and efficiently achieving diagnosis of abnormality and instruction of operation using the model. In the abnormality diagnosis system, an diagnosis model creating process unit creates a structured abnormality model expressing a structured abnormality of maintenance operation type to an alarm and apparatus event relating to the maintenance operation type by a graph network structure based on acquisition of maintenance operation data. And, by synthesizing the structured abnormality model with an existing structured abnormality model, the diagnosis model is updated.

Abstract: An inspection system (a secondary battery anomaly detection system) has a ROM which stores data including information about a V-dQ/dV curve upon initial charge of a reference secondary battery. At inspection of a secondary battery, upon initial charge from a power supply, an anomaly detecting unit uses an amount of stored electricity calculated from a current value detected by a current detecting unit to calculate a dQ/dV actual measurement value, which is a ratio of a changed amount of the amount of stored electricity to a changed amount of a voltage value detected by a voltage detecting unit, compares the calculated value and the information about the V-dQ/dV curve to determine whether the calculated value corresponds to a feature point on the curve, and detects an anomaly of the secondary battery (incapability of long-term capacity reliability) when the calculated value does not correspond to the feature point on the curve.

Abstract: The present invention relates to an apparatus abnormality monitoring method, and it provides a technology that can achieve accurate abnormality detection, cause diagnosis, and others. This system relates to monitoring of a newly-installed apparatus (T) among a plurality of similar apparatuses 1. In a judgment model creation module 2, for each of a plurality of (K) already-installed similar apparatuses (a, b and others), individual judgment models (prediction models) are created, and a meta prediction model for predicting a coefficient and an intercept of these prediction models from feature item values and others of each of the apparatuses 1 is created. From this meta prediction model, a prediction model dedicated to the apparatus (T) (judgment model including the prediction model) is produced. By using this judgment model, a judgment module 3T monitors the state of the apparatus (T) to perform abnormality detection.

Abstract: In an apparatus anomaly monitoring system, a model creation module (2) creates an ensemble of models formed of predictive models of an objective variable (Y) for each explanatory variable (X) based on regression analysis with using a plurality of state data items (DS) measured from an apparatus (1) to be a target. With using this, a monitoring execution module (3) monitors a state of the apparatus (1) to detect the anomaly. More particularly, the explanatory variables (X) are categorized into a collinearity item (XA) and an independency item (XB) to create an individual model for each of the collinearity items (XA) with using the collinearity item (XA) and the independency item (XB). With using the ensemble of these models, a predicted value of the objective variable (Y), an error span between the predicted value and a measurement value, an ensemble error span, and others are computed.

Abstract: Manufacturing management information relating to a module battery and single battery mounted therein which are obtained in a battery manufacturing process (1) is collected by a manufacturing quality information collection processing unit (4) and stored in a database (6). In a battery diagnosis system (3), when the module battery (2) under a usage environment is charged, an operation result processing unit (5) collects operation result information relating to the module (2) and stores the operation result information in a database (7). An operation result monitoring processing unit (8) determines whether the operation result information is abnormal, and supplies the result of the determination to a manufacturing/usage environment factor classification processing unit (9).

Abstract: The present invention relates to an apparatus abnormality monitoring method, and it provides a technology that can achieve accurate abnormality detection, cause diagnosis, and others. This system relates to monitoring of a newly-installed apparatus (T) among a plurality of similar apparatuses 1. In a judgment model creation module 2, for each of a plurality of (K) already-installed similar apparatuses (a, b and others), individual judgment models (prediction models) are created, and a meta prediction model for predicting a coefficient and an intercept of these prediction models from feature item values and others of each of the apparatuses 1 is created. From this meta prediction model, a prediction model dedicated to the apparatus (T) (judgment model including the prediction model) is produced. By using this judgment model, a judgment module 3T monitors the state of the apparatus (T) to perform abnormality detection.

Abstract: There is provided a technique related to an apparatus anomaly monitoring method and capable of achieving a model which can detect an anomaly with high accuracy, monitoring with using the model, and others. In an apparatus anomaly monitoring system, a model creation module (2) creates an ensemble of models formed of predictive models of an objective variable (Y) for each explanatory variable (X) based on regression analysis with using a plurality of state data items (DS) measured from an apparatus (1) to be a target. With using this, a monitoring execution module (3) monitors a state of the apparatus (1) to detect the anomaly. More particularly, the explanatory variables (X) are categorized into a collinearity item (XA) and an independency item (XB) to create an individual model for each of the collinearity items (XA) with using the collinearity item (XA) and the independency item (XB).

Abstract: A technique relating to an apparatus abnormality diagnosis system, capable of easily creating and adding/updating an diagnosis model with respect to an initial and new failure case, and appropriately and efficiently achieving diagnosis of abnormality and instruction of operation using the model. In the abnormality diagnosis system, an diagnosis model creating process unit creates a structured abnormality model expressing a structured abnormality of maintenance operation type to an alarm and apparatus event relating to the maintenance operation type by a graph network structure based on acquisition of maintenance operation data (S104). And, by synthesizing the structured abnormality model with an existing structured abnormality model, the diagnosis model is updated (S106).

Abstract: In an exposure process forming a predetermined circuit pattern of a semiconductor device on a wafer, a resist dimension of the resist pattern formed on a wafer and a focus position in the exposure process at a past time are measured. A resist dimension and a focus position of a wafer to which the exposure process is secondly performed are estimated by using measurement results of the measured resist dimension and focus position, and a focus offset value is calculated by using estimated values of the estimated resist dimension and focus position. Then, an exposure dose is calculated with considering this focus offset value, and a resist pattern is formed on the wafer to which the exposure process is performed by using the calculated exposure dose and focus offset value.

Abstract: In an exposure process forming a predetermined circuit pattern of a semiconductor device on a wafer, a resist dimension of the resist pattern formed on a wafer and a focus position in the exposure process at past time are measured, a resist dimension and a focus position of a wafer to which the exposure process is secondly performed are estimated by using measurement results of these measured resist dimension and focus position, and a focus offset value is calculated by using estimated values of these estimated resist dimension and focus position, and then, an exposure dose is calculated as considering this focus offset value, and a resist pattern is formed on the wafer to which the exposure process is performed by using these calculated exposure dose and focus offset value.

Abstract: In a method for determining a design parameter in a product design parameter determination supporting system, interaction between product function and design parameter is structured, a design parameter group with reduced interaction is extracted, an experiment design is prepared based upon the design parameter group, modeling of a quality influence degree between product function and design parameter is performed from an experiment result, and coordination process of a design parameter and estimation of a product function are performed with taking into account a manufacturing variation. By this means, it is possible to realize a system which can determine many design parameters extremely efficiently even in the product development where there are many design parameters and product functions to be handled and interactions are present between the design parameters and product functions.

Abstract: In a method for determining a design parameter in a product design parameter determination supporting system, interaction between product function and design parameter is structured, a design parameter group with reduced interaction is extracted, an experiment design is prepared based upon the design parameter group, modeling of a quality influence degree between product function and design parameter is performed from an experiment result, and coordination process of a design parameter and estimation of a product function are performed with taking into account a manufacturing variation. By this means, it is possible to realize a system which can determine many design parameters extremely efficiently even in the product development where there are many design parameters and product functions to be handled and interactions are present between the design parameters and product functions.

Abstract: A industrial equipment can easily change display contents and setting contents. In the industrial equipment, a display setting unit which makes a screen display does not hold data for the screen display. According to an operation instruction from an operator, the display setting unit reads a corresponding display data file, generates screen data based on the display data file, and displays the data. The display contents and the setting contents can be changed by changing a corresponding display data file only.

Abstract: A system for manufacturing a semiconductor device which predicts a difference in registration error between a circuit pattern and an overlay mark from a pattern dimension, illumination conditions and the wave aberration of an exposure lens, feeds a correction value based on the predicted difference back to an exposure device and modifies an overlay inspection data control limit.

Abstract: In lithographic (exposure) processing for semiconductor device fabrication, the task of extracting exposure parameters is performed by calculating exposure energy and focus offset using a test wafer for each exposure device, because fluctuations due to differences between exposure devices are large. For the fabrication of semiconductor devices in multiple-product small-lot production, the number of times the task of extracting exposure parameters has to be performed increases, so that the operation ratio of the exposure devices decreases, and the TAT of the semiconductor device fabrication increases. Moreover, as the miniaturization of semiconductor devices advances, differences between the exposure devices cause defects due to the exposure processing, and the yield of the semiconductor device fabrication decreases.

Abstract: A system for manufacturing a semiconductor device which predicts a difference in registration error between a circuit pattern and an overlay mark from a pattern dimension, illumination conditions and the wave aberration of an exposure lens, feeds a correction value based on the predicted difference back to an exposure device and modifies an overlay inspection data control limit.

Abstract: In lithographic (exposure) processing for semiconductor device fabrication, the task of extracting exposure parameters is performed by calculating exposure energy and focus offset using a test wafer for each exposure device, because fluctuations due to differences between exposure devices are large. For the fabrication of semiconductor devices in multiple-product small-lot production, the number of times the task of extracting exposure parameters has to be performed increases, so that the operation ratio of the exposure devices decreases, and the TAT of the semiconductor device fabrication increases. Moreover, as the miniaturization of semiconductor devices advances, differences between the exposure devices cause defects due to the exposure processing, and the yield of the semiconductor device fabrication decreases.