Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applications

This paper demonstrated the fabrication, characterization, data-driven modeling, and practical application of a 1D SnO₂ nanofiber-based memristor, in which a 1D SnO₂ active layer was sandwiched between silver (Ag) and aluminum (Al) electrodes. This device yielded a very high R_OFF : R_ON of ∼10⁴ (I_ON : I_OFF of ∼10⁵) with an excellent activation slope of 10 mV/dec, low set voltage of V_SET ∼ 1.14 V and good repeatability. This paper physically explained the conduction mechanism in the layered SnO₂ nanofiber-based memristor. The conductive network was composed of nanofibers that play a vital role in the memristive action, since more conductive paths could facilitate the hopping of electron carriers. Energy band structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claims reported in this paper. An machine learning (ML)–assisted, data-driven model of the fabricated memristor was also developed employing different popular algorithms such as polynomial regression, support vector regression, k nearest neighbors, and artificial neural network (ANN) to model the data of the fabricated device. We have proposed two types of ANN models (type I and type II) algorithms, illustrated with a detailed flowchart, to model the fabricated memristor. Benchmarking with standard ML techniques shows that the type II ANN algorithm provides the best mean absolute percentage error of 0.0175 with a 98% R² score. The proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adopting the same fabrication recipe, which gave satisfactory predictions. Lastly, the ANN type II model was applied to design and implement simple AND & OR logic functionalities adopting the fabricated memristors with expected, near-ideal characteristics.