Abstract
In the semiconductor sector, due to high demand but also strong and increasing competition, time to market and quality are key factors in securing significant market share in various application areas. Thanks to the success of deep learning methods in recent years in the computer vision domain, Industry 4.0 and 5.0 applications, such as defect classification, have achieved remarkable success. In particular, Domain Adaptation (DA) has proven highly effective since it focuses on using the knowledge learned on a (source) domain to adapt and perform effectively on a different but related (target) domain. By improving robustness and scalability, DA minimizes the need for extensive manual re-labeling or re-training of models. This not only reduces computational and resource costs but also allows human experts to focus on high-value tasks. Therefore, we tested the efficacy of DA techniques in semi-supervised and unsupervised settings within the context of the semiconductor field. Moreover, we propose the DBACS approach, a CycleGAN-inspired model enhanced with additional loss terms to improve performance. All the approaches are studied and validated on real-world Electron Microscope images considering the unsupervised and semi-supervised settings, proving the usefulness of our method in advancing DA techniques for the semiconductor field.
Abstract (translated)
在半导体行业中，由于需求高但竞争也十分激烈且日益加剧，市场进入时间和产品质量是确保在各种应用领域中占有显著市场份额的关键因素。近年来，深度学习方法在计算机视觉、工业4.0和5.0应用（如缺陷分类）等领域取得了显著成功。特别是域适应(Domain Adaptation, DA)技术已证明非常有效，因为它专注于利用一个特定领域的知识来调整并使其能在另一个相关但不同的领域中有效运作。通过提高鲁棒性和可扩展性，DA减少了对广泛的重新标注或模型再训练的需求，这不仅降低了计算和资源成本，还使人类专家能够将精力集中在高价值的任务上。 因此，在半导体行业的背景下，我们测试了域适应技术在半监督和无监督设置中的有效性。此外，我们提出了一种名为DBACS的方法，这是一种受到CycleGAN启发的模型，并增加了额外的损失项以提高性能。所有这些方法都基于真实世界的电子显微镜图像进行研究和验证，在无监督和半监督设置中证明了我们的方法在推进半导体领域域适应技术方面的实用性。
URL
https://arxiv.org/abs/2506.15260
