近幾年虛擬量測(Virtual Metrology, VM)於半導體產業與TFT-LCD產業之相關文獻履見不鮮。VM不僅能即時地提供製程之產品品質狀況，亦能增進先進製程控制(Advanced Process Control, APC)之效益。實際機台生產過程中，不免面臨定期維護(Preventive Maintenance, PM)機台、機台漂移(Drift)，或為了校正機台所需的實驗批(Design of Experiments, DOE)等狀況，若能蒐集到上述事件的重要樣本，將可對於VM模型所包含的資料特性更加完整，進而提升VM線上即時預測產品品質的準確性及穩健性。有鑑於此，本論文運用分群技術發展一套線上模型更新時所需之萃取重要樣本之機制，換言之，即從預測模型中剔除相對不重要的樣本，使預測模型內的資訊愈來愈豐富，進而提升VM之預測能力，此機制稱之為動態移動視窗(Dynamic Moving Window, DWM)。DMW的優點可使線上VM模型即時地吸收生產機台變異之重要資訊，且不會因時間因素而將重要樣本丟棄，未來若面臨過去已發生之機台變異時，VMS亦能準確地預測產品品質。實驗結果顯示，VM Model加入DMW機制後，能提供對於機台發生變化時，更佳之預測能力。

Over the past few years, virtual metrology (VM) has been widely developed and published in several VM related literature in the semiconductor and TFT-LCD industries. VM not only can provide the quality of semi-products in real time, but also can enhance the effectiveness of Advanced Process Control (APC). In the production process, events such as preventive maintenance (PM), tool drift, or using design of experiments (DOE) to calibrate the machine tools are essential. If samples related to the above important events can be collected and included in the VM models, the performance of VM can be enhanced and more robust for real-time online prediction. In this paper, we propose to use the clustering technique to keep important samples for the model refreshing scheme. In other words, VMS (Virtual Metrology System) will remove samples with similar characteristics from the conjecture model. In this way, the features of production process in the conjecture model will still be abundant even after samples are removed. The new scheme, named Dynamic Moving Window (DWM), has the advantage of absorbing the variation information of products in the online VM model, and important samples will not be discarded as time goes on. When the same machine variation has been encountered in the past, VMS can accurately provide prediction quality. This paper have shown that DMW scheme can provide VM Model with a more significant predictive capacity while the process tools have become unstable.

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