LAUSR

Artificial neural networks (ANNs) have strong learning and computing capabilities, and alleviate the problem of high power consumption of traditional von Neumann architectures, providing a solid basis for advanced image recognition, information processing, and low-power detection. Recently, a two-dimensional (2D) MoS2 field-effect transistor (FET) integrating a Zr-doped HfO2 (HZO) ferroelectric layer has shown potential for both logic and memory applications with low power consumption, which is promising for parallel processing of massive data. However, the long-term potentiation (LTP) characteristics of such devices are usually non-linear, which will affect the replacement of ANN weight values and degrade the ANN recognition rate. Here, we propose a dual-gate-controlled 2D MoS2 FET employing HZO gate stack with a crested symmetric structure to reduce power consumption. Improved nonlinearity of the LTP properties has been achieved through the electrical control of the dual gates. A recognition rate reaching 100% is obtained after 60 training epochs, and is 7.89% higher than that obtained from single-gate devices. Our proposed device structure and experimental results provide an attractive pathway towards high-efficiency data processing and image classification in the advanced artificial intelligence field.