Segmentation models in automated optical inspection of wire-bonded semiconductors are typically device-specific and must be re-trained when new devices or distribution shifts appear. We introduce AOI-SSL, a training-efficient framework for semantic segmentation of wire-bonded semiconductors by combining small-domain self-supervised pre-training of vision transformers with in-context inference that minimizes the need of labeled examples. We pre-train SOTA self-supervised algorithms in a small industrial inspection dataset and find that Masked Autoencoders are the most effective in this small-data setting, improving downstream segmentation while reducing the labeled fine-tuning effort. We further introduce in-context, patch-level retrieval methods that predict masks directly from dense encoder embeddings with negligible additional training. We show that, in this setting, simple similarity-based retrieval performs on par with more complex attention-based aggregation used currently in the literature. Furthermore, our experiments demonstrate that self-supervised pre-training significantly improves segmentation quality compared to training from scratch and to ImageNet pre-trained backbones under a fixed fine-tuning computational budget. Finally, the results reveal that retrieval based segmentation outperforms fine-tuning when targeting single device images, allowing for near-instant adaptation to difficult samples.

翻译：自动光学检测中引线键合半导体的分割模型通常具有器件特异性，当出现新器件或分布偏移时需要重新训练。本文提出AOI-SSL——一个面向引线键合半导体语义分割的高效训练框架，通过将小域自监督预训练视觉Transformer与上下文推理相结合，显著降低对标注样本的需求。我们在小型工业检测数据集上预训练了当前最优的自监督算法，发现掩码自编码器在小数据场景下最为有效，既能提升下游分割性能，又能减少标注微调工作量。进一步，我们引入上下文补丁级检索方法，可直接从密集编码器嵌入中预测掩码，且几乎无需额外训练。实验表明，在此场景下，简单的基于相似度的检索方法与文献中当前使用的复杂注意力聚合方法性能相当。此外，在固定微调计算预算下，自监督预训练相较从头训练和ImageNet预训练主干网络能显著提升分割质量。最终，结果揭示：在针对单器件图像时，基于检索的分割优于微调方法，可近乎即时地适应困难样本。