Optimization of structural and electrical properties of graphene-based TiO2 thin film device using Bayesian machine-learning approach

The optimization of laser -induced graphene (LIG) patterning is crucial for achieving desirable electronic properties in flexible electronic devices. In this study, we applied state-of-the-art automated parameter optimization techniques, specifically Bayesian optimization, to enhance the electrical resistance of LIG patterns. By iteratively optimizing the laser power, irradiation time, pressure, and gas type, we achieved minimum LIG resistance within eight batch configurations suggested by the Bayesian optimization (BO) approach. Our method eliminates the reliance on skilled operators, as the initial surrogate models were trained with random parameter evaluations. Notably, our system enables the optimization of material properties even when characterizations are only available outside the experiment loop. Furthermore, the surrogate model provided insights into the underlying mechanisms of LIG growth on quartz substrates. Through partial dependence analysis, we identified relevant physical domains for further investigation, leading to the discovery of negative capacitance and a correlation between structural and electrical properties in LIG. These findings were supported by XPS, Raman, and optical characterizations. Our approach streamlines the experimental design, reducing time and cost while accelerating materials research, and offers human -interpretable conclusions for a deeper understanding of LIG patterning processes.