In Situ Functional Monitoring of Aerosol Jet-Printed Electronic Devices Using a Combined Sparse Representation-Based Classification (SRC) Approach

Abstract

The goal of this work is in situ monitoring of the functional properties of aerosol jet-printed electronic devices. In pursuit of this goal, the objective is to develop a multiple-input, single-output (MISO) machine learning model to estimate the device functional properties in a near real-time fashion as a function of process parameters as well as 2D/3D features of line morphology. The aim is to use the MISO model for in situ estimation and thus, monitoring of line/device resistance in aerosol jet printing (AJP) process. To realize this objective, silver nanoparticle structures are printed by varying three process parameters: (i) sheath gas flow rate (ShGFR), (ii) exhaust gas flow rate (EGFR), and (iii) print speed (PS). Subsequently, line morphology is captured in situ using a high-resolution charge-coupled device (CCD) camera, mounted coaxial to the nozzle. Besides, utilizing 2D/3D quantifiers (introduced in the authors’ previous publications), the line morphology is further quantified, and the extracted features (e.g., line width, overspray, cross-sectional area, etc.) are fed as inputs to a novel sparse representation-based classification (SRC) model. The four-point probe method is used for measurement of resistance, and definition of a priori classification labels. The outcome of this research paves the way for future control of device functional properties in AJP process.