16. A method for automatic virtual metrology (AVM), comprising: instructing a first AVM server, by a model-creation server, to collect a plurality of historical measurement values and a plurality of sets of historical process data corresponding to historical measurement values required for building models; connecting said first AVM server to first metrology equipment for collecting said historical measurement values, and connecting said first AVM server to a first process apparatus for collecting said sets of historical process data; checking correlations between said sets of historical process data and said historical actual measurement values by said first AVM server; sending all of successfully-correlated measurement values and process data to said model-creation server by said first AVM server; performing a data-preprocessing step on the data required for building models by said model-creation server, thereby removing abnormal historical process data and historical measurement values; creating a DQI x model and a DQI y model by said model-creation server, wherein said DQI x model is used for computing respective DQI x values for the sets of process data producing workpieces, and said DQI x model is built in accordance with a principal component analysis (PCA) and a Euclidean distance (ED) algorithm; said DQI y model is used for computing a DQI y value for said first actual measurement value, wherein said DQI y model is built in accordance with adaptive resonance theory 2 (ART2) and normalized variability (NV); applying said DQI x model and said DQI y model to perform a data-sifting step by said model-creation server, thereby selecting enough correlated historical process data and historical measurement values needed for building models; applying the correlated historical process data and historical measurement values, by said model-creation server, to create a set of first VM models, said set of first VM models comprising: said DQI x model, said DQI y model, a conjecture model, a RI model and a GSI model, wherein said conjecture model is built in accordance with a conjecture algorithm, wherein said conjecture algorithm is a neural network (NN) algorithm, a multi-regression (MR) algorithm or a support vector machines (SVM) algorithm; a RI model including a reference prediction model for generating respective RI indexes for workpieces, wherein said reference prediction model is built in accordance with a reference algorithm, and said reference algorithm is different from said conjecture algorithm and is a multi-regression algorithm, a neural network algorithm, or a SVM algorithm; and said GSI model is used for computing respective GSI values for the set of process data, wherein said GSI model is built in accordance with a Mahalanobis distance algorithm; fanning out said set of first VM models to said first AVM server and a second AVM server so as to conduct virtual metrology on the workpieces being produced on said first process apparatus and a second process apparatus, respectively; performing a model-refreshing procedure on each set of first VM models in said first AVM server and said second AVM server; and providing VM services by said first AVM server and said second AVM server after completing their respective model-refreshing procedures.
16. A method for automatic virtual metrology (AVM), comprising: instructing a first AVM server, by a model-creation server, to collect a plurality of historical measurement values and a plurality of sets of historical process data corresponding to historical measurement values required for building models; connecting said first AVM server to first metrology equipment for collecting said historical measurement values, and connecting said first AVM server to a first process apparatus for collecting said sets of historical process data; checking correlations between said sets of historical process data and said historical actual measurement values by said first AVM server; sending all of successfully-correlated measurement values and process data to said model-creation server by said first AVM server; performing a data-preprocessing step on the data required for building models by said model-creation server, thereby removing abnormal historical process data and historical measurement values; creating a DQI x model and a DQI y model by said model-creation server, wherein said DQI x model is used for computing respective DQI x values for the sets of process data producing workpieces, and said DQI x model is built in accordance with a principal component analysis (PCA) and a Euclidean distance (ED) algorithm; said DQI y model is used for computing a DQI y value for said first actual measurement value, wherein said DQI y model is built in accordance with adaptive resonance theory 2 (ART2) and normalized variability (NV); applying said DQI x model and said DQI y model to perform a data-sifting step by said model-creation server, thereby selecting enough correlated historical process data and historical measurement values needed for building models; applying the correlated historical process data and historical measurement values, by said model-creation server, to create a set of first VM models, said set of first VM models comprising: said DQI x model, said DQI y model, a conjecture model, a RI model and a GSI model, wherein said conjecture model is built in accordance with a conjecture algorithm, wherein said conjecture algorithm is a neural network (NN) algorithm, a multi-regression (MR) algorithm or a support vector machines (SVM) algorithm; a RI model including a reference prediction model for generating respective RI indexes for workpieces, wherein said reference prediction model is built in accordance with a reference algorithm, and said reference algorithm is different from said conjecture algorithm and is a multi-regression algorithm, a neural network algorithm, or a SVM algorithm; and said GSI model is used for computing respective GSI values for the set of process data, wherein said GSI model is built in accordance with a Mahalanobis distance algorithm; fanning out said set of first VM models to said first AVM server and a second AVM server so as to conduct virtual metrology on the workpieces being produced on said first process apparatus and a second process apparatus, respectively; performing a model-refreshing procedure on each set of first VM models in said first AVM server and said second AVM server; and providing VM services by said first AVM server and said second AVM server after completing their respective model-refreshing procedures. 18. The method as claimed in claim 16 , further comprising: applying said sets of historical process data and historical actual measurement values in accordance with a cross validation's leave-one-out (LOO) method, to rebuild said conjecture model, said reference prediction model and said GSI model, and to compute respective VM conjecture errors, reference prediction errors and GSI values for each of said sets of historical process data, thereby computing a refreshing threshold of VM conjecture, a refreshing threshold of reference prediction and a refreshing threshold of GSI; inputting newly-collected process data to said conjecture model, said reference prediction model and said GSI model in said set of first VM models, thereby computing a phase-one VM value, a phase-one reference prediction value and a phase-one GSI value; defining a workpiece and several consecutive workpieces entering said first process apparatus before or after said workpiece as a plurality of consecutive refreshing samples; defining a first model-refreshing condition representing that the mean absolute percentage error (MAPE) values of the phase-one VM values of said consecutive refreshing samples are smaller than said refreshing threshold of VM conjecture; defining a second model-refreshing condition representing that the MAPE values of the phase-one reference prediction values of said consecutive refreshing samples are smaller than said refreshing threshold of reference prediction; defining a third model-refreshing condition representing that the phase-one GSI values of said consecutive refreshing samples are smaller than said refreshing threshold of GSI; and determining if all of said first model-refreshing condition, said second model-refreshing condition and said third model-refreshing condition are met simultaneously, thereby obtaining a determination result, wherein said model-refreshing procedure is completed when said determination result is true.