Abstract: A training apparatus and a training method for providing a sample size expanding model are provided. A normalizing unit receives a training data set with at least one numeric predictor factor and a numeric response factor. An encoding unit trains the training data set in an initial encoding layer and at least one deep encoding layer. A modeling unit extracts a mean vector and a variance vector and inputting the mean vector and the variance vector together into a latent hidden layer for obtaining the sample size expanding model. A decoding unit trains the training data set in at least one deep decoding layer and a last encoding layer. A verifying unit performs a verification of the sample size expanding model according to the outputting data set. A data generating unit generates a plurality of samples via the sample size expanding model.

Abstract: A method for analyzing a process output and a method for creating an equipment parameter model are provided. The method for analyzing the process output includes the following steps: A plurality of process steps are obtained. A processor obtains a step model set including a plurality of first step regression models, each of which represents a relationship between N of the process steps and a process output. The processor calculates a correlation of each of the first step regression models. The processor picks up at least two of the first step regression models to be a plurality of second step regression models whose correlations are ranked at top among the correlations of the first step regression models. The processor updates the step model set by a plurality of third step regression models, each of which represents a relationship between M of the process steps and the process output.

Abstract: A manufacture parameters grouping and analyzing method, and a manufacture parameters grouping and analyzing system are provided. The manufacture parameters grouping and analyzing method includes the following steps: A plurality of process factors are classified into a plurality of groups. In each of the groups, an intervening relationship between any two of the process factors is larger than a predetermined correlation value. In each of the groups, at least one representative factor is selected from each of the groups according to a plurality of outputting relationships of the process factors related to an output factor or a plurality of sample amounts of the process factors. Finally, the representative factor is used for various applications.

Abstract: A training apparatus and a training method for providing a sample size expanding model are provided. A normalizing unit receives a training data set with at least one numeric predictor factor and a numeric response factor. An encoding unit trains the training data set in an initial encoding layer and at least one deep encoding layer. A modeling unit extracts a mean vector and a variance vector and inputting the mean vector and the variance vector together into a latent hidden layer for obtaining the sample size expanding model. A decoding unit trains the training data set in at least one deep decoding layer and a last encoding layer. A verifying unit performs a verification of the sample size expanding model according to the outputting data set. A data generating unit generates a plurality of samples via the sample size expanding model.

Abstract: An analyzing method and an analyzing system for manufacturing data are provided. The analyzing method includes the following steps. A plurality of models each of which has a correlation value representing a relationship between at least one of a plurality of factors and a target parameter are provided. The models are screened according to the correlation values. A rank information and a frequency information of the factors are listed up according to the models. The factors are screened according to the rank information and the frequency information. The models are ranked and at least one of the models is selected.

Abstract: An analyzing method and an analyzing system for manufacturing data are provided. The analyzing method includes the following steps. A plurality of models each of which has a correlation value representing a relationship between at least one of a plurality of factors and a target parameter are provided. The models are screened according to the correlation values. A rank information and a frequency information of the factors are listed up according to the models. The factors are screened according to the rank information and the frequency information. The models are ranked and at least one of the models is selected.

Abstract: A manufacture parameters grouping and analyzing method, and a manufacture parameters grouping and analyzing system are provided. The manufacture parameters grouping and analyzing method includes the following steps: A plurality of process factors are classified into a plurality of groups. In each of the groups, an intervening relationship between any two of the process factors is larger than a predetermined correlation value. In each of the groups, at least one representative factor is selected from each of the groups according to a plurality of outputting relationships of the process factors related to an output factor or a plurality of sample amounts of the process factors. Finally, the representative factor is used for various applications.

Abstract: A method for analyzing a process output and a method for creating an equipment parameter model are provided. The method for analyzing the process output includes the following steps: A plurality of process steps are obtained. A processor obtains a step model set including a plurality of first step regression models, each of which represents a relationship between N of the process steps and a process output. The processor calculates a correlation of each of the first step regression models. The processor picks up at least two of the first step regression models to be a plurality of second step regression models whose correlations are ranked at top among the correlations of the first step regression models. The processor updates the step model set by a plurality of third step regression models, each of which represents a relationship between M of the process steps and the process output.