An Adaptive Deep Learning Framework for Fast Recognition of Integrated Circuit Markings

Fast recognition of integrated circuits markings is an essential but challenging task in electronic device manufacturing lines. This article develops an adaptive deep learning framework to facilitate the fast marking recognition of IC chips. The proposed framework contains four deep learning components, namely chip segmentation, orientation correction, character extraction, and character recognition. The four components utilize different convolutional neural network structures to guarantee excellent adaptivity to a wide range of IC types, and mitigate the influence of the low-quality chip images. In particular, the character extraction model is comprised of two improved label generation strategies and a proposed border correction method, so as to accommodate tiny scale chips and compactly printed markings. Experiments for a set of chip images from a real laptop manufacturing line demonstrate the superiority of the proposed framework to the state-of-the-art models and the effectiveness of handling a great diversity of chips.