Printed electronics (PE) is an emerging technology that provides attractive and complementary features compared to traditional wafer-scale silicon fabrication, such as flexible substrate and point-of-use ultralow-cost manufacturing. The low-cost manufacturing and larger feature sizes mandate reduced complexity in circuit size and also limited and transparent printing layers. This enables optical inspection for manufacturing defect detection, eliminating the need for electrical testing for gross defect detection. Therefore, the traditional problem of controllability and observability in logic testing can completely be alleviated. In this article, we present a learning-based method for optical inspection to detect defective transistors in transparent PE. The method leverages domain-specific as well as common inspection features extracted from optical images to detect defective transistors using supervised learning algorithms trained with real fabricated transistor images. The results show that the proposed method detects 95% of the defective transistors, which can significantly reduce the cost of the overall test flow.

中文翻译：

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使用基于学习的光学检测进行透明印刷电子产品的缺陷检测


印刷电子 (PE) 是一项新兴技术，与传统晶圆级硅制造（例如柔性基板和使用点超低成本制造）相比，它提供了有吸引力的互补功能。低成本制造和更大的特征尺寸要求降低电路尺寸的复杂性以及有限且透明的印刷层。这使得光学检查能够用于制造缺陷检测，从而无需进行电气测试来检测总体缺陷。因此，传统逻辑测试中的可控性和可观察性问题可以得到彻底缓解。在本文中，我们提出了一种基于学习的光学检测方法，用于检测透明 PE 中的缺陷晶体管。该方法利用从光学图像中提取的特定领域以及常见的检查特征，使用用真实制造的晶体管图像训练的监督学习算法来检测有缺陷的晶体管。结果表明，所提出的方法可以检测出 95% 的缺陷晶体管，可以显着降低整体测试流程的成本。