Flip-chip microbump vibration signals are characterized by microscale features, strong interference in acquired signals, and severe confusion of multi-modal components, which give rise to complicated mapping relationships of defect features and a remarkable drop in defect recognition accuracy. It severely restricts the accuracy of defect identification and industrial applicability, becoming a key bottleneck that urgently needs to be addressed in the field of micro-nano electronic manufacturing defect detection. To tackle the deficiencies of existing knowledge distillation, including insufficient exploitation of logical knowledge, weak utilization of inter-class relationships, and limited capacity to achieve accurate defect identification, this article proposes a feature logic space grouping distillation (FLSGD) algorithm. This algorithm analyzes explicit and implicit knowledge around the logic of knowledge transfer, and projects interactive information into three probability spaces, namely target space t, intra-class space o＼t and outer-class space c＼o. It decouples the traditional Kullback-Leibler (KL) divergence to realize directed learning of logical features. The subjective and objective weighting method is adopted to quantify the weights of the three spaces and determine the parameter guidance values, so as to flexibly regulate the focus of knowledge transfer. To verify the effectiveness of the proposed algorithm, experiments are conducted on two ultrasonic vibration datasets derived from PCB substrate chip and silicon-based dummy chips. A single/dual-source ultrasonic excitation detection platform is established for validation, which verifies that the target space plays a dominant role in FLSGD, while the intra-class and outer-class spaces serve auxiliary functions. Experimental results demonstrate that FLSGD outperforms mainstream feature distillation methods in terms of recognition accuracy across various teacher-student network combinations, achieving a maximum accuracy of 96.47% and 97.69% on the two datasets, respectively. FLSGD can effectively exploit the modal characteristics of vibration signals, enhance the perception of key features, improve defect recognition accuracy and accelerate model convergence speed without introducing additional computational modules or increasing computing power consumption, thus providing an effective solution for lightweight defect detection in industrial applications.