Dual-gate manipulation of a HfZrOx-based MoS2 field-effect transistor towards enhanced neural network applications

Abstract

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.