3. The system as claimed in claim 2 , wherein said set of VM models comprises: a conjecture model used for generating a first VM value of said first workpiece or a second VM value of said second workpiece, wherein said conjecture model is built in accordance with a conjecture algorithm, and said conjecture algorithm is a neural network (NN) algorithm, a multi-regression (MR) algorithm or a support vector machines (SVM) algorithm; a RI (Reliance Index) model used for generating respective RI values of said first VM value and said second VM value, wherein said RI 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; a GSI (Global Similarity Index) model used for computing respective GSI values for said set of first process data and said set of second process data, wherein the GSI model is built in accordance with a Mahalanobis distance algorithm; a DQI x (Process Data Quality Index) model used for computing respective DQI x values for said set of first process data and said set of second process data, wherein said DQI x model is built in accordance with a principal component analysis (PCA) and a Euclidean distance (ED) algorithm; and a DQI y (Metrology Data Quality Index) model used for computing DQI y values for said first actual measurement value and said second actual measurement value, wherein said DQI y model is built in accordance with adaptive resonance theory 2 (ART2) and normalized variability (NV).
3. The system as claimed in claim 2 , wherein said set of VM models comprises: a conjecture model used for generating a first VM value of said first workpiece or a second VM value of said second workpiece, wherein said conjecture model is built in accordance with a conjecture algorithm, and said conjecture algorithm is a neural network (NN) algorithm, a multi-regression (MR) algorithm or a support vector machines (SVM) algorithm; a RI (Reliance Index) model used for generating respective RI values of said first VM value and said second VM value, wherein said RI 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; a GSI (Global Similarity Index) model used for computing respective GSI values for said set of first process data and said set of second process data, wherein the GSI model is built in accordance with a Mahalanobis distance algorithm; a DQI x (Process Data Quality Index) model used for computing respective DQI x values for said set of first process data and said set of second process data, wherein said DQI x model is built in accordance with a principal component analysis (PCA) and a Euclidean distance (ED) algorithm; and a DQI y (Metrology Data Quality Index) model used for computing DQI y values for said first actual measurement value and said second actual measurement value, wherein said DQI y model is built in accordance with adaptive resonance theory 2 (ART2) and normalized variability (NV). 4. The system as claimed in claim 3 , wherein said first AVM server comprises: a control kernel used for monitoring the states of respective modules in said first AVM server, and controlling the operation procedure of each module in accordance with a dual-phase virtual metrology algorithm; a data pre-processing module used for evaluating the quality of said set of first process data and said first actual measurement value by using said DQI x model and said DQI y model; a pluggable driver interface (PDI); a data collection driver connected to said PDI for collecting said set of historical process, said historical measurement values, said set of first process data and said first actual measurement value, wherein the process data and measurement values collected are provided to said data pre-processing module via said PDI; a pluggable application interface (PAI); a VM-models storage module connected to the PAI for storing said set of VM models; a pluggable communication interface (PCI); and a communication agent connected to said PCI for communicating with said model-creation server.