A wafer bin map consists of a local chip containing key information and a global chip present in all patterns. The defect pattern shows a specific pattern shape on the wafer bin map and is defined based on the existing area information. Global information is not differentiated from local information in classification problems and is recognized as a major characteristic, so it affects the identification of the characteristics of defective patterns. In preparation for this, a method of extracting key local information has been proposed. In this paper, we propose a Skip Connections Denoising Autoencoder-based methodology to extract regional information of defect patterns. Randomly distributed chips are recognized as noise by defining anomaly scores based on the probability of each chip appearing in the wafer bin map. We propose a data transformation and reconstruction methodology for extracting local information based on the anomaly score, which is an uncertainty score index. Through the proposed methodology, it was confirmed that the main information that could not be extracted from the convolutional neural network (CNN) was extracted, and it was confirmed that the method proposed in this paper for WM-811K data is superior to the existing method.