Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization.

Inkjet printing is a powerful direct material writing process. It can be used to deposit microfluidic droplets in designated patterns at submicrometer resolution, which reduces materials usage. Nonetheless, predicting jetting characterizations is not easy because of the intrinsic complexity of the ink-nozzle-air interactions. Thus, inkjet processes are monitored by skilled engineers to ensure process reliability. This is a bottleneck in industry, resulting in high labor costs for multiple nozzles. To address this, we present a deep learning-based method for jetting characterizations. Inkjet printing is recorded by an in situ CCD camera and each droplet is detected by YOLOv5, a 1-stage detector using a convolutional neural network (CNN). The precision, recall, and mean average precision (mAP) at a 0.5 intersection over the union (IoU) threshold of the trained model were 0.86, 0.89, and 0.90, respectively. Each regression result for a detected droplet is accumulated in chronological order for each class of droplet and nozzle. The quantified information includes velocity, diameter, length, and translation, which can be used to synchronize multinozzle jetting and, eventually, the printed patterns. This demonstrates the feasibility of autonomous real-time process testing for large-scale electronics manufacturing, such as the high-resolution patterning of biosensor electrodes and QD display pixels while exploiting big data obtained from jetting characterizations.

The Effect of Pre-Stretched Substrate on the Electrical Resistance of Printed Ag Nanowires.

One-dimensional nanomaterials have drawn attention as an alternative electrode material for stretchable electronics. In particular, silver nanowires (Ag NWs) have been studied as stretchable electrodes for strain sensors, 3D electronics, and freeform-shaped electronic circuits. In this study, Ag NWs ink was printed on the pre-stretched silicone rubber film up to 40% in length using a drop-on-demand dispenser. After printing, silicone rubber film was released and stretched up to 20% as a cyclic test with 10-time repetition, and the ratios of the resistance of the stretched state to that of the released state (Rstretched/Rreleased) were measured at each cycle. For Ag NWs electrode printed on the pre-stretched silicone rubber at 30%, Rstretched/Rreleased at 10% and 20% strain was 1.05, and 1.57, respectively, which is significantly less than about 7 for Ag NWs at the 10% strain without pre-stretched substrate. In the case of 10% strain on the 30% pre-stretched substrate, the substrate is stretched and the contact points with Ag NWs were not changed much as the silicone rubber film stretched, which meant that Ag NWs may slide between other Ag NWs. Ag NWs electrode on the 40% pre-stretched substrate was stretched, strain was concentrated on the Ag NWs electrode and failure of electrode occurred, because cracks occurred at the surface of silicone rubber film when it was pre-stretched to 40%. We confirmed that printed Ag NWs on the pre-stretched film showed more contact points and less electric resistance compared to printed Ag NWs on the film without pre-stretching.

Deep-Learning-Based Microfluidic Droplet Classification for Multijet Monitoring.

Inkjet printing, the deposition of microfluidic droplets on a specified area, has gained increasing attention from both academia and industry for its versatility and scalability for mass production. Inkjet printing productivity depends on the number of nozzles used in a multijet process. However, droplet jetting conditions can vary for each nozzle due to multiple factors, such as the surface wetting condition of the nozzle, properties of the ink, and variances in the manufacturing of the nozzle head. For these reasons, droplet jetting conditions must be continuously monitored and evaluated by skillful engineers. The present study presents a deep-learning-based method to identify the droplet jetting status of a single-jet printing process. A convolutional neural network (CNN)-based on the MobileNetV2 model was employed with optimized hyperparameters to classify the inkjet frames containing images captured with a CCD camera. By accumulating the classified class data in order by frame time, the jetting conditions could be evaluated with high accuracy. The method was also successfully demonstrated with a multijet process, with a test time of less than a second per image.