Flip chips are widely used in electronic systems for defense, aerospace, and other applications where packaging reliability is critical. However, flip chip defect samples present a variety of defect types and few samples with labels in actual industrial applications. Therefore, flip chip intelligent defect detection faces the problems of poor model adaptability and weak generalization performance. As a solution to these problems, a dual-constraint centroid contrastive prototypical network (DCCPN) for flip chip defect detection under limited labeled data is proposed in this paper. First, a prototype-based supervised contrastive learning strategy is developed to construct the contrastive prototypical network, which increases the inter-class sparsity and intra-class compactness of features to acquire more discriminative features. Then, to address the susceptibility of the support set prototypes to outliers, dual constraints are imposed on the support set prototypes to calibrate and refine the prototypes. Defect detection experiments on flip chip vibration signals indicate that the present method is superior to other methods in the case of limited labeled samples.