Abstract: 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.

Abstract: 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.

Abstract: 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 monito

Abstract: 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. 2).

Abstract: The present invention relates to an apparatus, method, and reference sample for searching for molding conditions of an additive manufacturing apparatus based on a powder bed fusion method, and provides a search apparatus, a search method, and a reference sample that are remarkably more efficient and accurate than conventional counterparts. The reference sample for searching for the molding conditions is shaped so that it has a surface having aggregated punched holes formed by straight lines and curved lines involved in a fill region of a molding region (in-skin), a region for forming an overhang (down-skin), and a region for forming an outermost surface in the direction of molding height (up-skin). Slice data of the reference sample has at least two independent regions in an optional layer at a center in a lamination direction, and includes a small region and a large region. The small region has a width of 1 mm or less and is cut off from the outer edge of the reference sample.

Abstract: 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.

Abstract: 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 monito

Abstract: 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.

Abstract: Target processing conditions are searched while reducing a process development time and process development costs. A processing condition search device includes a learning database including a processing database storing target processing data for a target process and a reference processing database storing reference processing data for a reference process, a teachered learning execution unit that estimates an I/O model of the target process, which is an I/O model between a target description variable and a target objective variable, a transfer learning execution unit that estimates the I/O model of the target process using a reference I/O mode between a reference description variable and a reference objective variable, and the target processing data, and a transferability determination unit that determines whether the teachered learning execution unit or the transfer learning execution unit is used to estimate the I/O model of the target process.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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. 2).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.