Monolayer MoS2/WO3 Heterostructures with Sulfur Anion Reservoirs as Electronic Synapses for Neuromorphic Computing

Memristive devices based on two-dimensional (2D) semiconducting materials have emerged as highly promising neuromorphic devices due to their intrinsic atomic body and unique properties. However, the migration and redistribution of anions induces built-in electric field at 2D materials/electrode interface. It inevitably leads to nonlinearity and saturation of conductance change, which are the key challenges of 2D materials based synaptic devices to achieve high accuracy neuromorphic applications. In this work, we report a vertical heterostructure formed by monolayer CVD-grown MoS2 and WO3 films, in which the WO 3 films serve as anions reservoir to steadily absorb and release sulfur anions, thus successfully overcoming the hurdles of nonlinearity and limited conductance states. We experimentally demonstrate a nearly linear change in conductance (similar to 1.1) and as high as 130 (similar to 2(7)) weight states, which is a record among 2D materials-based synapses. Simulations prove that artificial neural network with MoS2/WO3 heterostructure synapses achieves a significantly improved learning accuracy of 93.2% in MNIST handwritten digits, demonstrating the dual benefits of linearity and multilevels caused by the anion reservoir. In addition, the essential synaptic behaviors, such as potentiation/depression, paired pulse facilitation, spike-rate-dependent plasticity as well as transformation from short-term plasticity to long-term plasticity are implemented in the heterostructure device. The introduction of anion reservoir opens an effective approach to overcome the limitations of 2D materials and enhance the performance of neuromorphic devices for high-precision neuromorphic computing.

Automated summary

The study introduces a vertical heterostructure composed of MoS2 and WO3 films, with WO3 acting as an anion reservoir to address the nonlinearity and limited conductance states in 2D materials-based synaptic devices. Experimental results show nearly linear conductance change and up to 130 weight states, with simulations demonstrating significantly improved learning accuracy of 93.2% in artificial neural networks.