Hitachi, Ltd.

Hitachi High-tech Corporation

Hyakka Nakada

Takeshi Ohmori

Masaru Kurihara

Tatehito Usui

Naoyuki Kofuji

Masayoshi Ishikawa

Yutaka Okuyama

Naoto Takano

Shinji Matsushita

Hirotsugu Kawanaka

Noboru Saitou

High-accuracy dimension extraction for a complicated shape possibly appearing through semiconductor processing. A starting point and an endpoint are placed on the periphery of a graphic shape including a combination of multiple ellipses, and a curve unicursally drawn on the periphery between the two points is used as a shape mode, thereby describing a contour of an intended structure.

Contour analysis apparatus, processing condition determination system, shape estimation system, semiconductor device manufacturing system, search apparatus, and data structure used in them

A search apparatus includes a processor and a memory. The processor receives a molding result of a reference sample manufactured by the additive manufacturing apparatus. The processor calculates predicted values from a predictive model. The processor determines whether the evaluation target values are achieved by the measured values. The reference sample has at least three smooth surfaces and a surface having aggregated punched holes formed by straight lines and curved lines that are involved in three types of regions to be set as the conditions.

Additive manufacturing condition search apparatus, additive manufacturing condition search method, and reference sample

A model learning unit learns a prediction model on the basis of learning data, a target setting unit sets a target output parameter value by interpolating between a goal output parameter value and an output parameter value which is the closest to the goal output parameter value in output parameter values in the learning data, a processing condition search unit estimates input parameter values which corresponds to the goal output parameter value and the target output parameter value, a model learning unit updates the prediction model by using a set of the estimated input parameter value and an output parameter value which is a result of processing that a processing device performs as additional learning data.

Plasma processing apparatus including predictive control

Search device, searching method, and plasma processing apparatus

To efficiently search a processing condition of giving a desired target processing result, there is provided a processing condition determination system including a processing apparatus that processes a sample, a processing monitor system that monitors the state of the processing in the processing apparatus, and an analysis system that sets the processing condition of the processing apparatus of giving a target processing result, wherein the system includes a processing condition and result database that stores a set of an explanatory variable that is a processing condition under which the processing apparatus processes a sample and an objective variable that is the processing result obtained by the processing apparatus' processing the sample, and when the processing apparatus processes the sample under the processing condition set using the correlation model derived from the database and it is determined that a probability of failure occurrence becomes high, based on the monitor data of the processing monitor system, the processing apparatus stops the processing under the present processing condition and the analysis system resets a new processing condition.

Processing condition determination system and processing condition searching method

An object of the present invention is to provide a technique that allows automatically generating a processing recipe from an inspection image even when the inspection image varies by being affected by an imaging condition of a processing device. A processing recipe generation device according to the present invention generates a converted image in which components relying on the imaging condition of the inspection image are reduced and generates a processing recipe using a target image generated using a conversion condition same as that of the converted image (see FIG.).

Processing recipe generation device

A computer for determining a control parameter of processing to be performed on a sample includes: a memory unit configured to store a first model indicating a correlation between a first processing output obtained by measuring a first sample used for manufacturing, on which the processing is performed and a second processing output obtained by measuring a second sample that is easier to measure than the first sample and on which the processing is performed, and a second model indicating a correlation between a control parameter of the processing performed on the second sample and the second processing output; and an analysis unit configured to calculate a target control parameter of the processing performed on the first sample based on a target processing output as the target first processing output, the first model, and the second model.

Computer, method for determining processing control parameter, substitute sample, measurement system, and measurement method

The efficiency of operation in a semiconductor processing apparatus is improved. In order to search an input parameter value to be set in a semiconductor processing apparatus for processing into a target processed shape, a predictive model indicating a relationship between an input parameter value and an output parameter value is generated based on the input parameter value and the output parameter value which is a measured value of a processing result processed by setting the input parameter value in the semiconductor processing apparatus. In this case, when the measured value of the processing result processed by the semiconductor processing apparatus is the defective data, the predictive model is generated based on the input parameter value causing defective data and defective substitute data obtained by substituting the measured value which is the defective data.

Search device, search method and plasma processing apparatus

A sample simulates a processing state of a semiconductor sample and is measured by a measurement device. The sample includes: a first surface formed at a first height when viewed from a sample surface; a second surface formed at a second height higher than the first height; and a plurality of inflow parts which allow a particle for performing processing on the first surface to flow between the first surface and the second surface. The processing by the particle flowing from the inflow parts is superimposed in at least a part of a region to be processed on the first surface, and the region where the processing is superimposed on the first surface is measured by the measurement device.

Substitute sample, method for determining control parameter of processing, and measurement system

A system for determining a processing procedure including a plurality of processes for controlling an object, the system includes a learning unit for performing a learning process for determining a processing condition of each of a plurality of processes, and the learning unit acquires a physical quantity correlating with a state of the object on which a process has been performed under a predetermined processing condition, from a device for controlling the object on the basis of the processing procedure, calculates a pseudo state corresponding to the state of the object on the basis of the physical quantity, performs a learning process using a value function, and determines a processing condition of each of the plurality of processes to achieve a target state of the object.

System and method of determining processing condition

A retrieval apparatus includes a processor and a memory and retrieves a condition given to a semiconductor treatment apparatus. The processor receives a processing result of a semiconductor, a condition corresponding to the processing result, a target value for treating the semiconductor, and a retrieval region. A prediction model is generated indicating a relationship between the condition and the processing result based on a set value of the condition in the retrieval region, and the processing result; calculates a predicted value, performs a demonstration test, acquires an actually measured value, outputs the predicted value as a set value when the actually measured value reaches the target value. When the actually measured value does not reach the target value, the prediction model is updated by applying the predicted value and the actually measured value to the set value and the processing result, respectively.

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Tokyo, Japan

Hyakka Nakada