Performance improvement of MoS2/graphene heterostructures based FET by tuning mobility and threshold voltage using APTES

•APTES boosts mobility and reduces threshold voltage in MoS2/graphene FETs.•Graphene electrodes lower Schottky barrier for high-performance MoS2 transistors.•APTES passivation improves device stability and prevents electron trapping.•Enhanced FETs show potential for neuromorphic computing and logic applications. 2D materials have been intensively explored because of their remarkable electrical properties, with a special focus devoted to the fabrication of lateral heterostructures. Two-dimensional materials like molybdenum disulphide (MoS2) have been shown to make field effect transistors (FETs) with high current on–off ratios. However, carrier mobility in back gate MoS2 FETs is often low(0.5–20 cm2/Vs), which limits the overall device performance. Here, we report a novel low Schottky barrier transistor based on graphene, MoS2 and Self-assembled monolayer (SAMS) that utilizes vertical heterostructures in which the channel is composed of MoS2/graphene vertical heterostructures that uses graphene as the electrodes. Self-assembled monolayers of Aminopropyltriethoxysilane (APTES) serve a dual purpose, used for passivation and as an n-type dopant for MoS2, significantly improving the electrical properties. Our experimental and theoretical results show that, with the deposition of APTES on the substrate, there is an increase in mobility from 103 to 135 cm2/Vs, along with the reduction in the threshold voltage from 5.04 to 1.05 V, which is attributed to APTES passivation, which prevents electron trapping and de-trapping, a significant factor determining variation in threshold voltage (ΔVTH). Density functional theory (DFT) calculations support the experimental results and demonstrate that the introduction of APTES doping in MoS2 induces n-type doping in the material, hence improving the performance of the device. The combination of graphene electrodes along with the APTES passivationon substrateholds the promise for reliable and efficient synaptic applications in neuromorphic computing technologies, as well as next-generation complementary logic devices.