Machine vision methodology for inkjet printing drop sequence generation and validation

Inkjet printing technology for printed microelectronics suffers from a number of non-idealities due to unwanted ink flow on the substrate. This can be mitigated and pattern fidelity can be improved by using an optimized drop placement sequence in contrast to the standard raster-scanning approach. However, it is challenging to auto-generate such printing sequences for complex printed patterns. Here, the generation and evaluation of the printing sequence are turned into a computer-vision problem. The desired printed pattern is taken as an input image and converted into a printing sequence using contour, symmetric, and matrix sequencing and corner compensation. After printing, pattern defects are detected by automated image processing to evaluate the printed pattern against the designed ground truth image and to determine the best possible algorithm for printing sequence generation for different pattern types. The machine vision-based experimental approach identifies the best solutions for solving the printing and defect optimization problem in terms of precision, recall, and accuracy. This methodology will enable the automated design of electronic circuits for applications such as wearable sensors, low-cost radio-frequency identification tags, or flexible displays.