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

Eunsik Choi¹, Suwon Choi², Kunsik An²

¹Digital Transformation R&D Department, Korea Institute of Industrial Technology (KITECH), Ansan 15588, Republic of Korea.

ACS Applied Materials & Interfaces

|March 26, 2024

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A deep learning method automates inkjet printing characterization, enabling real-time process monitoring and reducing labor costs. This AI approach enhances reliability for electronics manufacturing.

Area of Science:

Materials Science and Engineering
Artificial Intelligence
Manufacturing Technology

Background:

Inkjet printing offers precise material deposition but faces challenges in predicting jetting due to complex ink-nozzle-air interactions.
Current manual monitoring by engineers is labor-intensive and limits scalability in industrial applications.
Ensuring process reliability in high-volume manufacturing, such as electronics, requires automated characterization methods.

Purpose of the Study:

To develop a deep learning-based method for autonomous, real-time jetting characterization in inkjet printing.
To overcome the limitations of manual monitoring and reduce labor costs associated with process reliability checks.
To enable synchronized multinozzle jetting for advanced manufacturing applications.

Main Methods:

Keywords:

convolutional neural network (CNN)inkjet printing jetting characteristics microfluidics multijet synchronization object detection quantum dot (QD)

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Utilized an in situ CCD camera to record inkjet printing processes.
Employed YOLOv5, a convolutional neural network (CNN)-based object detection model, to identify individual droplets.
Developed a regression analysis to quantify droplet parameters like velocity, diameter, length, and translation.

Main Results:

The YOLOv5 model achieved high performance with precision (0.86), recall (0.89), and mean average precision (mAP) of 0.90 at a 0.5 IoU threshold.
Quantified droplet characteristics were accumulated chronologically for each nozzle and droplet class.
Demonstrated the ability to synchronize multinozzle jetting based on real-time characterization data.

Conclusions:

The deep learning approach enables autonomous real-time process testing for inkjet printing.
This method significantly reduces labor costs and enhances process reliability in large-scale manufacturing.
Facilitates high-resolution patterning for applications like biosensor electrodes and QD display pixels by leveraging big data from jetting characterizations.