Hitachi High-tech Corporation

Hitachi Power Solutions Co., Ltd.

Hiatachi Power Solutions Co., Ltd.

Pushe Zhao

Masaru Kurihara

Toujirou Noda

Shigeyoshi Chikuma

Junichi Tanaka

Takeshi Ohmori

Yutaka Okuyama

A dimension measurement apparatus that automatically corrects a deviation of a contour without operator determination. The dimension measurement apparatus includes: a model learning unit that obtains a learning cross-sectional image and learning labels attached to different regions of the learning cross-sectional image and generates a deep learning model for image region division using the learning cross-sectional image and the learning labels; a model estimation unit that applies the model to a newly input target image and labels each independent region; a contour correction unit that detects a contour of each region using the target image and the labels, sets a representative point on the contour of the region, moves each representative point according to a movement rule, and repeats movement of the representative point until contour correction is complete; and a dimension measurement unit that measures a dimension of a device cross-sectional structure using the corrected contour.

Dimension measurement apparatus, semiconductor manufacturing apparatus, and semiconductor device manufacturing system

A preprocessor into which time-series sensor data is input includes a correspondence table associating sensor data with a physical unit. A physical extraction unit that obtains sensor data, and specifies a physical unit based on the correspondence table. A latent variable generation unit specifies a nonlinear function from the physical unit, and generates a latent variable for applying the sensor data to the specified nonlinear function. A latent variable addition unit obtains time-series sensor data and sets the data as a variable, applies the obtained time-series sensor data to the latent variable to generate time-series data and sets the data as a variable, and adds the generated time-series data to the obtained time-series sensor data to generate a data set of a plurality of variables. Then a relational expression estimation-processing unit that analyzes a relationship between the variables of the data set, and generates a linear relational expression.

Preprocessor and abnormality predictor diagnosis system

A preprocessor includes a sensor data storage part that is connected to an abnormality sign diagnosing device and stores multi-dimensional sensor data, a data analysis processing part that calculates a variable value by using the multi-dimensional sensor data stored in the sensor data storage part, an analysis data storage part that stores the variable value calculated by the data analysis processing part, and an analysis data addition processing part that combines the multi-dimensional sensor data stored at the sensor data storage part and the variable value stored in the analysis data storage part and outputs a combined result to the abnormality sign diagnosing device.

Preprocessor of abnormality sign diagnosing device and processing method of the same

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Tokyo, Japan

Pushe Zhao