Tokyo Electron Limited

Takuro Tsutsui

Motoi Okada

Kei Sano

Junya Sato

Junnosuke Maki

Nao Akashi

Atsushi Suzuki

Takayuki Kita

Kiwamu Tsukino

Atsushi Ohta

Masaomi Toyonaga

Yuta Haga

Mistsuteru Yano

Hiroyuki Sato

A virtual metrology apparatus, a virtual metrology method, and a virtual metrology program that allow a highly accurate virtual metrology process to be performed is provided. A virtual metrology apparatus includes an acquisition unit configured to acquire a time series data group measured in association with processing of a target object in a predetermined processing unit of a manufacturing process, and a training unit configured to train a plurality of network sections by machine learning such that a result of consolidating output data produced by the plurality of network sections processing the acquired time series data group approaches inspection data of a resultant object obtained upon processing the target object in the predetermined processing unit of the manufacturing process.

Virtual metrology apparatus, virtual metrology method, and virtual metrology program

An abnormality detection device trains a model using multiple network sections each configured to process acquired time series data sets and a concatenation section configured to combine output data output from each of the multiple network sections and to output, as a combined result, a result of combining the output data output from each of the multiple network sections. The trained model is then applied to adapt a unit of process performed during manufacture of a processed object.

Abnormality detecting device and abnormality detecting method

A state determination device determines the state of a drive mechanism configured to operate while holding a substrate in a substrate processing apparatus. The state determination part includes: an acquisition part configured to acquire operation data for the drive mechanism; a model generation part configured to generate a monitoring model for the drive mechanism by executing machine learning using an auto-encoder based on normal operation data that is derived from the operation data acquired by the acquisition part when the drive mechanism is operating normally; and a first determination part configured to determine the state of the drive mechanism based on first output data obtained by inputting, to the monitoring model, evaluation data that is derived from the operation data acquired by the acquisition part when the drive mechanism is being evaluated.

State determination device, state determination method, and computer-readable recording medium

According to an aspect of the present disclosure, a substrate state determining apparatus includes: an image capturing unit that captures an image of a substrate placed on a stage; a learning unit that executes a machine learning using training data in which information indicating a state of the substrate is attached to the image of the substrate, so as to generate a substrate state determination model in which the image of the substrate is taken as an input and a value related to the state of the substrate corresponding to the image of the substrate is taken as an output; and a determination unit that determines the state of the substrate corresponding to the image of the substrate captured by the image capturing unit, using the substrate state determination model generated by the learning unit.

Substrate state determining apparatus, substrate processing apparatus, model generating apparatus, and substrate state determining method

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Sapporo, Japan

Takuro Tsutsui