A Strategy for Real-time Suppressing the Fluctuation of DFB Chip Micro-character Spotting Accuracy in Optical Device Packaging

Effectively, economically and long-term stably real-time suppressing the fluctuation of micro-character spotting accuracy (mCSA) for a submillimeter-level Distributed Feedback Laser(DFB) chip in the complex industrial environment(CIE) is an essential but challenging task in optical device packaging. This article develops an end-to-end MOCRNet with one-stage frameworks (OSF), data enhancement and defect preprocessing. Specifically, with embedding the proposed text defect detector(TDD) and the text flip detection (TFD), the OSF has replaced the previous traditional multi-stage frameworks for improving long-term accuracy and efficiency. Moreover, a data enhancement technique called the environmental noise revivification method (ENRM) is applied to obtain expanded sample images with environmental noise to suppress small fluctuations by training the model; Additionally, large and small fluctuations caused by defects in the character area and on characters are effectively suppressed by the Background Defect Removal Module (BDRM) and TDD, respectively. Experiments from a real-world optical device packaging line based on the proposed MOCRNet industrial intelligent microscope system reached a mCSA of better than 99.6% and accordingly decost the labor to 86% compared to 98.8% and 72% in the latest previous work, respectively. The results demonstrate the superiority of the proposed MOCRNet to the state-of-the-art models, as well as the effectiveness and accuracy, and stability for handling a great fluctuation of mCSA from submillimeter-level chips with defects and noise during the optical device packaging in the CIE.