Quantifying Printing Quality for Printed Electrodes via Deep Learning and Spatial Association: Empowering Process Optimization

Abstract

Process optimization is a critical element in manufacturing, especially in the field of flexible and printed electronic (FPE) devices, particularly by a roll-to-roll (R2R) gravure printing. This technology is essential for the production of next-generation electronic devices due to its high throughput rates and cost-effective fabrication. However, the assessment of R2R gravure printing outcomes still relies on human visual inspection, introducing subjectivity susceptible to human fatigue. To address this challenge, a novel approach is proposed to quantify printing quality in real-time using deep learning and spatial association. This not only reduces the need for human labor in evaluating the performance of the R2R gravure printing system but also ensures objective quantifiability. Quantification facilitates optimal process settings through response surface methodology, as printing quality can be expressed numerically. The article demonstrates the application of deep learning for quantitatively analyzing printed patterns in real-time using the gate layer in the FPE devices as a reference sample, showing its effectiveness in optimizing the R2R gravure printing process.