8. A method for automatic virtual metrology (AVM), comprising: performing a model-creation step for building a set of first VM models by using a plurality of historical measurement values and a plurality of sets of historical process data corresponding to said historical measurement values, wherein said set of first VM models comprises: a conjecture model 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; collecting a set of first process data of a workpiece from a process apparatus; after the collection of said set first process data of said workpiece from said process apparatus is completed, performing a phase-one VM step, wherein said phase-one VM step comprises: performing a phase-one VM computation step for computing a phase-one VM value (VM 1 ) of said workpiece by inputting said set of first process data to said set of first VM models; and performing a phase-two VM step when a first actual measurement value of said workpiece is obtained, said phase-two VM step comprising: performing a retraining-deciding step for determining if a retraining step is performed, wherein said retraining step is performed when an instruction of model refreshing is true, said retraining step comprising: retraining or tuning a set of second VM models by using said set of first process data and said first actual measurement value to replace the oldest data in said sets of historical process data and said historical measurement value, wherein the model types and their building methods are the same for said set of first VM models and said set of second VM models; and replacing said set of first VM models by said set of second VM models to compute another phase-one VM value for another workpiece entering said process apparatus subsequently.
8. A method for automatic virtual metrology (AVM), comprising: performing a model-creation step for building a set of first VM models by using a plurality of historical measurement values and a plurality of sets of historical process data corresponding to said historical measurement values, wherein said set of first VM models comprises: a conjecture model 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; collecting a set of first process data of a workpiece from a process apparatus; after the collection of said set first process data of said workpiece from said process apparatus is completed, performing a phase-one VM step, wherein said phase-one VM step comprises: performing a phase-one VM computation step for computing a phase-one VM value (VM 1 ) of said workpiece by inputting said set of first process data to said set of first VM models; and performing a phase-two VM step when a first actual measurement value of said workpiece is obtained, said phase-two VM step comprising: performing a retraining-deciding step for determining if a retraining step is performed, wherein said retraining step is performed when an instruction of model refreshing is true, said retraining step comprising: retraining or tuning a set of second VM models by using said set of first process data and said first actual measurement value to replace the oldest data in said sets of historical process data and said historical measurement value, wherein the model types and their building methods are the same for said set of first VM models and said set of second VM models; and replacing said set of first VM models by said set of second VM models to compute another phase-one VM value for another workpiece entering said process apparatus subsequently. 12. The method as claimed in claim 8 , wherein said model-creation step further comprises: collecting said sets of historical process data from a process apparatus; and collecting said historical measurement values from metrology equipment, wherein said historical process data and said historical measurement values are correlated to the same workpieces.