Preservation of Surface Conductivity and Dielectric Loss Tangent in Large-Scale, Encapsulated Epitaxial Graphene Measured by Noncontact Microwave Cavity Perturbations.

Regarding the improvement of current quantized Hall resistance (QHR) standards, one promising avenue is the growth of homogeneous monolayer epitaxial graphene (EG). A clean and simple process is used to produce large, precise areas of EG. Properties like the surface conductivity and dielectric loss tangent remain unstable when EG is exposed to air due to doping from molecular adsorption. Experimental results are reported on the extraction of the surface conductivity and dielectric loss tangent from data taken with a noncontact resonance microwave cavity, assembled with an air-filled, standard R100 rectangular waveguide configuration. By using amorphous boron nitride (a-BN) as an encapsulation layer, stability of EG's electrical properties under ambient laboratory conditions is greatly improved. Moreover, samples are exposed to a variety of environmental and chemical conditions. Both thicknesses of a-BN encapsulation are sufficient to preserve surface conductivity and dielectric loss tangent to within 10% of its previously measured value, a result which has essential importance in the mass production of millimeter-scale graphene devices demonstrating electrical stability.

Low-Temperature Direct Growth of Amorphous Boron Nitride Films for High-Performance Nanoelectronic Device Applications.

We successfully demonstrated the improvement and stabilization of the electrical properties of a graphene field effect transistor by fabricating a sandwiched amorphous boron nitride (a-BN)/graphene (Gr)/a-BN using a directly grown a-BN film. The a-BN film was grown via low-pressure chemical vapor deposition (LPCVD) at a low growth temperature of 250 °C and applied as a protection layer in the sandwiched structure. Both structural and chemical states of the as-grown a-BN were verified by various spectroscopic and microscopic analyses. We analyzed the Raman spectra of Gr/SiO2 and a-BN/Gr/a-BN structures to determine the stability of the device under exposure to ambient air. Following exposure, the intensity of the 2D/G-peak ratio of Gr/SiO2 decreased and the position of the G and 2D peaks red-shifted due to the degradation of graphene. In contrast, the peak position of encapsulated graphene is almost unchanged. We also confirmed that the mobility of a-BN/Gr/a-BN structure is 17,941 cm2/Vs. This synthetic strategy could provide a facile way to synthesize uniform a-BN film for encapsulating various van der Waals materials, which is beneficial for future applications in nanoelectronics.

Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications.

Although two-dimensional (2D) nanomaterials are promising candidates for use in memory and synaptic devices owing to their unique physical, chemical, and electrical properties, the process compatibility, synthetic reliability, and cost-effectiveness of 2D materials must be enhanced. In this context, amorphous boron nitride (a-BN) has emerged as a potential material for future 2D nanoelectronics. Therefore, we explored the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications by fabricating a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device. The redox-active Ag and boron vacancies enhance the mixed electrochemical metallization and valence change conduction mechanism. The synthesized a-BN switching layer was characterized using several analyses. The fabricated memory devices exhibited bipolar resistive switching with low set and reset voltages (+0.8 and -2 V, respectively) and a small operating voltage distribution. In addition, the switching voltages of the device were modeled using a time-series analysis, for which the Holt's exponential smoothing technique provided good modeling and prediction results. According to the analytical calculations, the fabricated Ag/a-BN/Pt device was found to be memristive, and its MRS ability was investigated by varying the compliance current. The multilevel states demonstrated a uniform resistance distribution with a high endurance of up to 104 direct current (DC) cycles and memory retention characteristics of over 106 s. Conductive atomic force microscopy was performed to clarify the resistive switching mechanism of the device, and the likely mixed electrochemical metallization and valence change mechanisms involved therein were discussed based on experimental results. The Ag/a-BN/Pt memristive devices mimicked potentiation/depression and spike-timing-dependent plasticity-based Hebbian-learning rules with a high pattern accuracy (90.8%) when implemented in neural network simulations.