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In semiconductor manufacturing processes, spatial defect patterns on semiconductor wafers can provide useful information to quality engineers regarding the root causes of abnormalities. As early detection of process problems increases the wafer yield, the automatic recognition of defect patterns is crucial. It its intrinsic assumption that a decluttering of noise defects helps in accurately classifying defect patterns. However, most existing noise detection methods require the pre-determination of parameters that significantly affect the detection performance. In this paper, we propose a parameter-free detection framework called BAND, which refers to BAgging Noise Detectors, to solve this problem. By adopting the framework of bootstrap aggregating (bagging), which combines the detection results from multiple outlier detection algorithms, the proposed framework ensures the diversity of randomly chosen parameter values while maintaining an accurate detection of noise defects. Based on the estimated distributions of outlierness scores, a group of outliers is discriminated from the other group of inliers via spectral clustering. This procedure enables the proposed framework to be parameter-free. Moreover, numerical experiments based on synthetic and real-world datasets are presented to demonstrate the effectiveness of the proposed framework.