For the current wafer sawing process, the wafers in the same lot are inspected at the end of the entire process. Therefore, a defect, such as chipping, occurs during processing will only be detected until the end of the process, which is too late and may cause massive defects. If Automatic Virtual Metrology (AVM) is implemented in the wafer sawing process, when chippings occur and are detected, its chipping amount can be predicted by AVM on-line and in real time. Also, AVM's individual similarity index (ISI) analysis can be applied to identify the root cause of chipping. As a result, this root cause can be fixed to avoid generating defects in the subsequent wafers. However, chipping won't happen to all wafers. Since the AVM system deals mainly with the regression problem, it cannot classify whether a wafer is chipped or not. Hence, there is a need to predict wafer-chipping occurrence before applying AVM to the wafer sawing process. To solve the above mentioned problem, the wafer sawing qualitymonitoring is divided into two stages. An Automated Classification Scheme (ACS) based on ensemble learning is developed in Stage I to pre-determine whether a wafer is chipped. If chipping is detected, then proceed to Stage II for the AVM system to predict the chipping amount and identify the root cause that results in this chipping. With the so-calledACS-plus-AVMscheme, theAVMapplication in the wafer sawing process can be realized.
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