The manufacturing of sensors often requires the precise deposition of small volumes of liquid. In order to do this, expensive industrial equipment is often used, giving this form of manufacturing a high financial barrier to entry. As standard desktop inkjet printers operate by precisely depositing small volumes of paper onto a substrate, typically paper, the goal of the project was to modify a desktop inkjet printer, the Epson EcoTank ET-8500, to allow it to be used to deposit small droplets of fluid onto a CMOS sensor chip.

This report covers the design process of the hardware modifications that were necessary in order to allow the Epson EcoTank ET-8500 to print onto CMOS sensor chips packaged in a QFP- 44 package. These modifications include a custom chip tray to safely house the chips as they go into the printer, as well as an external fluid system to allow the user to easily use ink other than the stock ink provided by Epson or to put a cleaning fluid into the system in order to clear out any residue deposited by the inks used.

The results obtained indicated high levels of precision, and the system’s accuracy was sufficient for the sensor chips used. The external fluid system was also tested and successfully allowed the printer to work with fluids not placed into the built-in tanks.

The Epson ET-8500 inkjet printer was modified to deposit precise quantities of ink on CMOS chips, enabling the fabrication of capacitive sensors. This project explores the potential of using affordable, consumer-grade printing technology for advanced sensor development, typically requiring expensive equipment. The research focused on understanding the interaction between printed ink and environmental variables such as humidity, light, and temperature. Three measurement setups were developed to detect capacitance changes under varying conditions. The setups for light and humidity successfully generated usable data, while the temperature setup needs redesigning for consistency. Key findings include the exponential relationship between humidity and capacitance in water-based inks. Light exposure generally decreased capacitance, except for white, zinc oxide and tin oxide under UV light. A machine learning model, using a neural network with a cross-entropy loss function, effectively identified inked electrodes but requires more variance in the datasets.