In the semiconductor manufacturing process, Electrical Die Sorting (EDS) is a post-production process used to assess the quality of each chip on the wafer. The results from EDS testing are visualized as a wafer bin map (WBM), which is used for quality control purposes, such as the identification of defective wafers. Recently, deep learning has emerged as a prominent approach for identifying defects in wafers. However, data on defects in the semiconductor industry remain scarce. In this paper, we propose a semi-supervised learning method, TripletMatch, which utilizes triplet loss for unlabeled data. The proposed method extends the FixMatch framework and considers Mixup to smooth decision boundaries. Our experimental results demonstrate the superiority of TripletMatch over various recent deep-learning-based methods and loss functions.