Hitachi, Ltd.

Renesas Electronics Corporation

Toshiharu Miwa

Yasuhiro Yoshitake

Kenji Tamaki

Shunichi Matsumoto

Tetsuya Yamazaki

Seiji Ishikawa

Junko Konishi

Chizu Matsumoto

Hidekimi Fudo

Toshihide Kawachi

Shigenori Yamashita

Takeshi Tashiro

Manufacturing management information relating to a module battery and single battery mounted therein which are obtained in a battery manufacturing process () is collected by a manufacturing quality information collection processing unit () and stored in a database (). In a battery diagnosis system (), when the module battery () under a usage environment is charged, an operation result processing unit () collects operation result information relating to the module () and stores the operation result information in a database (). An operation result monitoring processing unit () determines whether the operation result information is abnormal, and supplies the result of the determination to a manufacturing/usage environment factor classification processing unit (). When the result of the determination indicates that the operation result information is abnormal, the manufacturing/usage environment factor classification processing unit () diagnoses, on the basis of the manufacturing management information relating to the module battery in the database (), whether the abnormality of the operation result information is caused by a manufacturing factor or a usage environment factor, and the result of the diagnosis is created and displayed by the diagnosis result creation processing unit ().

Diagnosis system and diagnosis method for lithium ion secondary battery

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.

Apparatus abnormality diagnosis method and system

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. In a judgment model creation module, 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 apparatusesis 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 moduleT monitors the state of the apparatus (T) to perform abnormality detection.

Apparatus abnormality monitoring method and system

In an apparatus anomaly monitoring system, a model creation module () 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 () to be a target. With using this, a monitoring execution module () monitors a state of the apparatus () 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.

Apparatus anomaly monitoring method and system

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.

Manufacturing method and manufacturing system of semiconductor device

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.

Method for determining parameter of product design and its supporting system

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.

Overlay inspection apparatus for semiconductor substrate and method thereof

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. In an improved method of exposure processing for semiconductor devices, the exposure energy and focus offset according to the illumination parameters for an exposure device and optical projection system, using information regarding the projection lens aberrations of a plurality of exposure devices, the photoresist parameters, and the circuit pattern information, as determined beforehand, are calculated using an optical development simulator, and the exposure processing is carried out using an exposure device, selected from a plurality of exposure devices, in which the process window is within a certain tolerance value.

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Yokohama, Japan

Toshiharu Miwa