This paper reports the first effort at developing a convolutional neural network (CNN) based image regression model to replace impedance spectroscopy (IS). In our study, theCNN model learned the features of inkjet-printed electrode images that are dependent on the printing and sintering of nanomaterials and quantitatively predicted the resistance and capacitance of the equivalent circuit of the inkjet-printed lines. The image-based impedance spectroscopy (IIS) is expected to be the cornerstone as a revolutionary approach to electronics research and development enabled by deep neural networks.

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 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 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.