Deep Learning Neural Network Approach for Correlation between Print Parameters and Realized Electrical Performance and Geometry on Ink-Jet Platform

The use of printed electronics is increasing rapidly and replacing the traditional manufacturing techniques, especially in the consumer electronics sector. In this paper, a closed-loop deep learning approach for correlation of the print parameters with realized electrical performance and geometry estimations on an ink-jet platform is modeled. To print reliable and very fine conductive traces, an estimation of the changes in the print parameters and the realized print dimension is necessary. The inks used for this analysis are both particle and particle-free silver inks, and the comparison of the same is also studied. A closed-loop control algorithm is used to attain the desired electrical and geometrical values by changing the print parameters without any user intervention. This is achieved by an automatic print parameter sensing system using a camera that captures the print to identify the geometry and dimension of the same. Once the realized print parameters are identified, a deep learning neural network regression model based on these parameters is used to predict the desired input print parameters which are used to achieve the desired geometry and dimension of the print. These new parameter values are passed on to the printing software to optimize the print and attain the desired geometry and printing characteristics. This closed-loop system employs a print characteristics sensing system using a camera, a deep learning neural network regression model to predict the new print parameters, and an auto-update system that changes the values in the printing software. These combinations of the system are used to correlate the print parameters with the realized electrical performance and geometry of the print on an ink-jet printing platform.