Defect Detection in Transparent Printed Electronics Using Learning-Based Optical Inspection

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.