The semiconductor process cannot avoid defects due to its complex and diverse processes. In particular, wafer can be said to be the core of semiconductor manufacturing because they are directly related to the productivity of semiconductors. Therefore, detecting and classifying defects on wafers can help engineers address the root cause of defects and improve yield. In this paper, we propose a convolutional autoencoder using skip connection for wafer map defect classification. First, the encoder and decoder are designed by constructing a convolutional block. And connect the symmetrical blocks with skip connection. Finally, the training data of the classifier is encoded using the weights of the learned encoder. The loss of the model was successfully reduced with skip connection, and improved performance was obtained by reusing the encoder.