Accelerated deep-learning-based process monitoring of microfluidic inkjet printing

Kim, Seong Jae; Choi, Eunsik; Won, Dong-Yeon; Han, Gyu Seok; An, Kunsik; Kang, Kyung Tae; Kim, Sanha

doi:10.1016/j.cirpj.2023.07.010

Cited by 3 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, machine learning-based image processing can enable classification, object detection, and tracking in applications where real-time assessment is essential. − Convolutional neural networks (CNN) have made it possible to deal with image data with high efficiency, using shared weights and maintaining a structure with highly correlated local features between adjacent pixels. , Recently, this has been applied, inter alia, for classification and object detection on microfluidic systems. ,, Doing so has allowed nonexperts to evaluate microfluidics processes or sensor readouts for real-time assessments. In addition, there have been efforts for experts to optimize aerosol jet printing , and inkjet printing along with labeling of inkjet printed patterns − using machine learning-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…46,47 Recently, this has been applied, inter alia, for classification and object detection on microfluidic systems. 39,48,49 Doing so has allowed nonexperts to evaluate microfluidics processes or sensor readouts for real-time assessments. In addition, there have been efforts for experts to optimize aerosol jet printing 50,51 and inkjet printing 52 along with labeling of inkjet printed patterns 53−55 using machine learning-based methods.…”

Section: ■ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Choi,

et al. 2024

ACS Appl. Mater. Interfaces

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

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

Choi,

et al. 2024

ACS Appl. Mater. Interfaces

Self Cite

View full text Add to dashboard Cite

show abstract

Image-based identification of optical quality and functional properties in inkjet-printed electronics using machine learning

Polomoshnov,

Reichert,

Rettenberger

et al. 2024

J Intell Manuf

View full text Add to dashboard Cite

We propose a novel image-analysis based machine-learning approach to the fully-automated identification of the optical quality, of functional properties, and of manufacturing parameters in the field of 2D inkjet-printed test structures of conductive traces. To this end, a customizable modular concept to simultaneously identify or predict dissimilar properties of printed functional structures based on images is described and examined. An application domain of the concept in the printing production process is outlined. To examine performance, we develop a dataset of over 5000 test structures containing images and physical characteristics, which are manufactured using commercially available materials. Functional test structures are fabricated via a single-nozzle vector-based inkjet-printing system and thermally sintered. Physical characterization of electrical conductance, image capturing, and evaluation of the optical quality of the test structures is done by an automatic in-house built measurement station. Conceptionally, the design of a convolutional neural network is described to identify the optical quality and physical characteristics based only on acquired images. A mathematical apparatus that allows assessment of the identification accuracy is developed and described. The impact of printing resolution, of emerging defects in the geometry of printed structures, and of image quality and color space on the identification accuracy is analyzed. Quality groups related to the printing resolution that affect identification accuracy are determined. Supplementarily, we introduce not yet reported classification of processes related to the fabrication of printed functional structures, adopted from the process analytical technology.

show abstract

Low-cost inkjet-printed nanostructured biosensor based on CRISPR/Cas12a system for pathogen detection

Carota,

Bonini,

Urban

et al. 2024

Biosensors and Bioelectronics

View full text Add to dashboard Cite

Accelerated deep-learning-based process monitoring of microfluidic inkjet printing

Cited by 3 publications

References 34 publications

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

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

Image-based identification of optical quality and functional properties in inkjet-printed electronics using machine learning

Low-cost inkjet-printed nanostructured biosensor based on CRISPR/Cas12a system for pathogen detection

Contact Info

Product

Resources

About