PZT-Enabled MoS<sub>2</sub> Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing
Jing Chen, Yeqing Zhu, Xuechun Zhao, Zhenghua Wang, Kai Zhang, Zheng Zhang, Mingyuan Sun, Shuai Wang, Yu Zhang, Lin Han, Xiaoming Wu, Tian‐Ling Ren
Abstract
Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the “Boltzmann tyranny”, which restricts SS to 60 mV decade –1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS 2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade –1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from −7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.