Abstract: An information processing apparatus includes a generation unit that generates simulation data including a plurality of combinations of unprocessed data of a workpiece and processed data of the workpiece after a process is performed on the workpiece under a predetermined process condition. Each of the plurality of combinations includes the unprocessed data and the processed data when the process is performed with a plurality of pattern densities for each of a plurality of mask shapes. The information processing apparatus further includes a derivation unit that derives simulation parameters of a shape simulator based on a closeness between predicted data that is predicted by inputting the unprocessed data included in the simulation data to the shape simulator, and the processed data combined with the unprocessed data.

Abstract: With respect to an inference method performed by at least one processor, the method includes inputting, by the at least one processor, into a learned model, non-processed object image data of a second object and data related to a second process for the second object, and inferring, by the at least one processor using the learned model, processed object image data of the second object on which the second process has been performed. The learned model has been trained so that an output obtained in response to non-processed object image data of a first object and data related to a first process for the first object being input approaches processed object image data of the first object on which the first process has been performed.

Abstract: A mechanism of predicting a change in process values with respect to a control target and using a prediction result is provided. A management apparatus includes a prediction model unit, with respect to which an input-output relationship between multivariate control values at a time T with respect to a control target and multivariate process values at a time T+?T with respect to the control target has been learned; and an optimization model unit configured to seek multivariate control values of the time T that minimize respective differences between the multivariate process values at the time T+?T output from the prediction model unit and corresponding target values, and control the control target using the multivariate control values of the time T that have been sought.

Abstract: A management system for managing a substrate manufacturing process includes agent units configured to monitor a state of a substrate processing device that performs the substrate manufacturing process and detect a predetermined event, and transmission paths configured to, when a predetermined event is detected in any one of the agent units, transmit and receive information between the agent units based on the detected event, wherein the one agent unit derives an instruction to the substrate processing device based on the information transmitted and received via the transmission paths so that an index value of the substrate manufacturing process is optimized.

Abstract: A global optimum solution of a simulation parameter is derived using a shape simulator. A parameter deriving device includes a generating unit configured to generate a plurality of combinations of data indicating pre-processing shapes and data indicating post-processing shapes of substrates processed under a same processing condition, data indicating a pre-processing shape or a post-processing shape being different from data indicating a pre-processing shape or a post-processing shape included in another combination in the plurality of combinations, and a deriving unit configured to derive a value of a simulation parameter of a shape simulator that minimizes a sum of respective differences between data indicating predicted post-processing shapes and data indicating corresponding post-processing shapes, the predicted post-processing shapes being predicted by inputting the data indicating the pre-processing shapes included in the plurality of combinations into the shape simulator.

Abstract: A model management system, a model management method, and a model management program that efficiently manage models applied to a substrate manufacturing process is provided. The model management system separately manages the models applied to the substrate manufacturing process in three or more layers, and includes a first management unit configured to manage a model at a predetermined layer, and one or more second management units configured to manage one or more models at a layer one level lower than the predetermined layer.

Abstract: A data collection system includes: a first substrate processing apparatus having a first processing space, a second substrate processing apparatus having a second processing space, and a data collection apparatus connected to the first substrate processing apparatus and the second substrate processing apparatus. The data collection apparatus is configured to compare observed data observed when substrates having the same or similar shapes are processed under the same processing conditions in the first processing space and the second processing space, respectively, and calculate a correction amount for correcting the observed data observed when being processed in the second processing space, and correct the observed data observed when being processed in the second processing space based on the correction amount, and collect corrected observed data, when searching for a processing condition by processing substrates while changing the processing condition in the second processing space.

Abstract: An information processing device includes: a recording means storing model data that reproduces a change of a workpiece, the model data being constructed, as an effect of process treatment, from differential data between initial state data and end state data of the workpiece, the end state data representing state of the workpiece to which the process treatment is applied under a predetermined process condition; an input receiving means for receiving an input of the initial state data and target end state data of the workpiece; a predicting means for predicting the end state data from the received initial state data, by using the model data and a combination of multiple model data in the recording means; and a determining means for determining a process condition of process treatment to be applied to the workpiece, based on a proximity between the predicted end state data and the target end state data.

Abstract: An information processing device includes: a recording means storing model data that reproduces a change of a workpiece, the model data being constructed, as an effect of process treatment, from differential data between initial state data and end state data of the workpiece, the end state data representing state of the workpiece to which the process treatment is applied under a predetermined process condition; an input receiving means for receiving an input of the initial state data and target end state data of the workpiece; a predicting means for predicting the end state data from the received initial state data, by using the model data and a combination of multiple model data in the recording means; and a determining means for determining a process condition of process treatment to be applied to the workpiece, based on a proximity between the predicted end state data and the target end state data.

Abstract: An information processing device includes: a recording means storing model data that reproduces a change of a workpiece, the model data being constructed, as an effect of process treatment, from differential data between initial state data and end state data of the workpiece, the end state data representing state of the workpiece to which the process treatment is applied under a predetermined process condition; an input receiving means for receiving an input of the initial state data and target end state data of the workpiece; a predicting means for predicting the end state data from the received initial state data, by using the model data and a combination of multiple model data in the recording means; and a determining means for determining a process condition of process treatment to be applied to the workpiece, based on a proximity between the predicted end state data and the target end state data.

Abstract: With respect to an inference method performed by at least one processor, the method includes inputting, by the at least one processor, into a learned model, non-processed object image data of a second object and data related to a second process for the second object, and inferring, by the at least one processor using the learned model, processed object image data of the second object on which the second process has been performed. The learned model has been trained so that an output obtained in response to non-processed object image data of a first object and data related to a first process for the first object being input approaches processed object image data of the first object on which the first process has been performed.

Abstract: An information processing device includes: a recording means storing model data that reproduces a change of a workpiece, the model data being constructed, as an effect of process treatment, from differential data between initial state data and end state data of the workpiece, the end state data representing state of the workpiece to which the process treatment is applied under a predetermined process condition; an input receiving means for receiving an input of the initial state data and target end state data of the workpiece; a predicting means for predicting the end state data from the received initial state data, by using the model data and a combination of multiple model data in the recording means; and a determining means for determining a process condition of process treatment to be applied to the workpiece, based on a proximity between the predicted end state data and the target end state data.

Abstract: A data processing apparatus includes: a first storage part that stores an analysis result that specifies each region when a feature space is divided such that a distribution of each data group associated with a predetermined step of a manufacturing process in the space is classified according to an effect calculated for each data group in the predetermined step; a second storage part that stores models each of which outputs the effect corresponding to each region, in association with each region, when the data groups classified into each region of the feature space are inputted; and an execution part for performing a simulation processing by using, among the models, a model stored in association with one region when a new data group associated with the predetermined step is acquired and when the one region into which the acquired new data group is classified is determined based on the analysis result.