In-Situ Metrology for Roll-to-Roll Microcontact Printing via Condensation Figures and YOLOv8

In-situ metrology of the pre-etching geometry of printed patterns is critical for roll-to-roll microcontact printing (R2R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>CP) quality control. In the previous works, condensation figures (CFs) were successfully used to visualize the nanometer-thick printed layers in R2R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>CP. However, applying CFs for real-time monitoring and controlling the R2R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>CP remains highly challenging. This is primarily due to the difficulty in extracting printed features from CFs, as pattern segmentation is often time-consuming and inaccurate due to the complexity and instability of the droplet size and intensity distribution. To solve this problem, we leverage the outstanding efficiency and accuracy of the you-only-look-once computer vision model to measure printed patterns from droplet-only CFs by segmenting them into printed and nonprinted areas. To address the difficulty of collecting CFs of real R2R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>CP with annotated labels for segmentation tasks, we develop a simulation pipeline to generate, augment, and label synthetic CFs for training deep image segmentation models. Experimental results on real R2R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>CP processes show that the proposed method achieves a 94.4% accuracy in line-width estimation, based on comparisons with postetching measurements, and outperforms other state-of-the-art deep learning and classic segmentation techniques. Finally, the method was integrated in an R2R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>CP system, enabling fully automated in-situ metrology at up to 15 CFs per second with 1080p resolution, providing real-time feedback for closed-loop quality control.