Energy-Efficient, Scalable Single-Layer MoS₂–Based Synaptic Field-Effect Transistors

Neuromorphic computing has emerged as a promising strategy for overcoming the von Neumann bottleneck by enabling energy-efficient parallel information processing. To realize such systems, it is crucial to develop artificial synaptic devices that are both energy-efficient and highly scalable. In this study, we present a single-layer MoS2-based synaptic field-effect transistor (FET) with a high-κ top-gate dielectric stack for low-power, nonvolatile synaptic operations. The absence of a blocking layer simplifies the fabrication process while maintaining reliable memory characteristics. Synaptic weights are effectively modulated through the trapping and detrapping of electrons within a HfO2 layer. The device exhibited stable long-term potentiation (LTP) and depression (LTD) with excellent endurance and reproducibility. Furthermore, the experimentally measured synaptic characteristics were implemented in a software-based deep neural network, achieving a recognition accuracy of 95.9% on the MNIST handwritten digit classification task. These findings highlight the potential of single-layer MoS2 synaptic transistors as scalable energy-efficient neuromorphic building blocks.