Abstract: Embodiments of the present disclosure provide semiconductor device structures and methods of forming the same. The structure includes a first source/drain region disposed in a PFET region and a second source/drain region disposed in an NFET region. The second source/drain region comprises a dipole region. The structure further includes a first silicide layer disposed on and in contact with the first source/drain region, a second silicide layer disposed on and in contact with the first silicide layer, and a third silicide layer disposed on and in contact with the dipole region of the second source/drain region. The first, second, and third silicide layers include different materials. The structure further includes a first conductive feature disposed over the first source/drain region, a second conductive feature disposed over the second source/drain region, and an interconnect structure disposed on the first and second conductive features.

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: 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.

Abstract: A nonparametric method for measuring a clustered level of time rank in binary data is provided. A sample set of engineering data is classified into a target group and a reference group, and a rank is set to each sample in a chronological order. A minimum rank and a maximum rank are obtained from the target group, by which a characteristic period is defined. In the characteristic period, an average rank values of the target group and an average rank value of the reference group are calculated. After creating a dummy sample set, the dummy sample set is incorporated into an analysis data set and a new rank is set based on a comparison result of the average rank value of the target group and the average rank value of the reference group, and the minimum rank and the maximum rank of the characteristic period to obtain adjusted test data. A Mann-Whitney U test is executed on the adjusted test data to obtain a clustered level index of time rank in binary data.

Abstract: A nonparametric method for measuring a clustered level of time rank in binary data is provided. A sample set of engineering data is classified into a target group and a reference group, and a rank is set to each sample in a chronological order. A minimum rank and a maximum rank are obtained from the target group, by which a characteristic period is defined. In the characteristic period, an average rank values of the target group and an average rank value of the reference group are calculated. After creating a dummy sample set, the dummy sample set is incorporated into an analysis data set and a new rank is set based on a comparison result of the average rank value of the target group and the average rank value of the reference group, and the minimum rank and the maximum rank of the characteristic period to obtain adjusted test data. A Mann-Whitney U test is executed on the adjusted test data to obtain a clustered level index of time rank in binary data.