Addressing the issues of complex surface defect types and minute defect sizes in electronic components that are difficult to detect, this paper proposes an electronic component surface defect detection model based on an improved reverse distillation network. Firstly, the model adopts an unsupervised learning approach, effectively mitigating the dependency on a large amount of annotated data. Secondly, by employing a reverse distillation network architecture, it alters the traditional mode of one-way guidance from the teacher model to the student model in distillation networks, thereby enhancing the model's adaptability in surface defect detection tasks. Furthermore, a receptive field attention convolutional module is introduced into the student decoder of the reverse distillation network to bolster the model's ability to detect minute defects. Lastly, the cosine similarity is utilized as the loss function to train the student network and the bottleneck module. Experimental results using a self-constructed dataset for electronic component surface defect detection demonstrate significant improvements in detection accuracy, achieving 86.7% and 89.1% in AUROC at the image and pixel levels, respectively, and 69.4% in AUPRO at the pixel level.