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.
10. The method as claimed in claim 8 , wherein said set of first VM models further comprises: a RI model for generating the reliant index of said VM values, wherein said RI model includes a reference prediction model, 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 a GSI model used for computing a GSI value for said set of first process data, wherein said GSI model is built in accordance with a Mahalanobis distance algorithm; said model-creation step further comprises: 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; said phase-one VM computation step further comprises: inputting said set of first process data to said reference prediction model and said GSI model in said set of first VM models, thereby computing a phase-one reference prediction value and a phase-one GSI value of said workpiece; and said phase-two VM step further comprises: when said instruction of model refreshing is true, defining said workpiece and several consecutive workpieces entering said process apparatus before and/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 instruction of model refreshing is set to false when said determination result is true, otherwise said instruction of model refreshing is true.