Reducing the sampling rate to as low as possible is a high priority for many factories to reduce production cost. Automatic-Virtual-Metrology (AVM) based Original Intelligent Sampling Decision (Original ISD) scheme had been previously developed for reducing the sampling rate and sustaining the VM accuracy. However, the desired sampling rate of the Original ISD scheme is fixed and set manually. Hence, whenever the VM accuracy gets worse, it cannot dynamically increase the desired sampling rate in the Original ISD scheme. As a consequence, it would take more time to collect enough samples for improving the VM accuracy. Moreover, when the VM accuracy performs well all the time, it cannot automatically decrease the desired sampling rate in Original ISD, which may result in unnecessary waste. Accordingly, this paper proposes a preliminary study of a Dynamic ISD scheme to dynamically and automatically modify the sampling rate online and in real time. The Dynamic ISD scheme can monitor the VM accuracy on line as well as update the VM models in real time for maintaining the VM accuracy when the VM accuracy becomes poor. Also, the Dynamic ISD scheme can automatically reduce the sampling rate while the VM accuracy performs well.

Wafer inspection plays a significant role in monitoring the quality of production wafers. However, it requires measuring tools and additional cycle time to do real metrology, which is costly and time-consuming. Therefore, reducing sampling rate to as low as possible is a high priority for many factories to reduce production cost. The most common way for inspecting process quality is to apply periodic sampling. If a manufacturing process is stable, then virtual metrology (VM) may be applied for monitoring the quality of wafers while real metrology is unavailable. Nevertheless, if a production variation occurs between periodic samplings, no real metrology is available during this period for updating the VM models, which may result in un-reliable VM predictions. The authors have developed the automatic virtual metrology (AVM) system for various VM applications. Therefore, this paper focuses on applying various indices of the AVM system to develop an Intelligent Sampling Decision (ISD) scheme for reducing sampling rate while VM accuracy is still sustained.