Bayesian Optimization of Low-Temperature Nonthermal Plasma Jet Sintering of Nanoinks

In response to the escalating demand for flexible devices in applications such as wearables, sensors, and touch panels, there is a need for innovative fabrication approaches for devices made from nanomaterial-based inks. Subsequent to ink deposition, a pivotal stage in device manufacturing typically involves high-temperature sintering, posing challenges for heat-sensitive substrates. Nonthermal plasma jet sintering utilizing an atmospheric pressure dielectric barrier discharge (DBD) plasma jet enables sintering at room temperature and standard pressure, facilitating the sintering of printed nanoparticle films without compromising substrate or film surface integrity. However, determining optimal plasma jet sintering conditions can be challenging due to multiple processing variables with intricate interrelationships. This work employed Bayesian optimization (BO) and machine learning (ML) to identify optimal values for seven primary plasma jet sintering variables. Optimization yielded a 99.2% increase in the measured electrical conductivity for plasma jet-sintered indium tin oxide (ITO) films after five rounds of experiments. Moreover, the optimal sintering conditions achieved an electrical conductivity that was 81.4% of conventional furnace sintering at 300 °C, but was three times faster and with a peak substrate temperature below 47 °C. This result demonstrates the prospect of applying BO to optimize processing techniques for emerging low-temperature requirements.