High-Performance Molybdenum Disulfide Field-Effect Transistors with Spin Tunnel Contacts

Molybdenum disulfide has recently emerged as a promising two-dimensional semiconducting material for nanoelectronic, optoelectronic, and spintronic applications. Here, we investigate the field-effect transistor behavior of MoS₂ with ferromagnetic contacts to explore its potential for spintronics. In such devices, we elucidate that the presence of a large Schottky barrier resistance at the MoS₂/ferromagnet interface is a major obstacle for the electrical spin injection and detection. We circumvent this problem by a reduction in the Schottky barrier height with the introduction of a thin TiO₂ tunnel barrier between the ferromagnet and MoS₂. This results in an enhancement of the transistor on-state current by 2 orders of magnitude and an increment in the field-effect mobility by a factor of 6. Our magneto­resistance calculation reveals that such integration of ferromagnetic tunnel contacts opens up the possibilities for MoS₂-based spintronic devices

Thermally Driven Multilevel Non-Volatile Memory with Monolayer MoS₂ for Brain-Inspired Artificial Learning

The demands of modern electronic components require advanced computing platforms for efficient information processing to realize in-memory operations with a high density of data storage capabilities toward developing alternatives to von Neumann architectures. Herein, we demonstrate the multifunctionality of monolayer MoS2 memtransistors, which can be used as a high-geared intrinsic transistor at room temperature; however, at a high temperature (>350 K), they exhibit synaptic multilevel memory operations. The temperature-dependent memory mechanism is governed by interfacial physics, which solely depends on the gate field modulated ion dynamics and charge transfer at the MoS2/dielectric interface. We have proposed a non-volatile memory application using a single Field Effect Transistor (FET) device where thermal energy can be ventured to aid the memory functions with multilevel (3-bit) storage capabilities. Furthermore, our devices exhibit linear and symmetry in conductance weight updates when subjected to electrical potentiation and depression. This feature has enabled us to attain a high classification accuracy while training and testing the Modified National Institute of Standards and Technology datasets through artificial neural network simulation. This work paves the way toward reliable data processing and storage using 2D semiconductors with high-packing density arrays for brain-inspired artificial learning