A van der Waals Synaptic Transistor Based on Ferroelectric Hf0.5Zr0.5O2 and 2D Tungsten Disulfide. (8th May 2020) - Chen, Li; Wang, Lin; Peng, Yue; Feng, Xuewei; Sarkar, Soumya; Li, Sifan; Li, Bochang; Liu, Liang; Han, Kaizhen; Gong, Xiao; Chen, Jingsheng; Liu, Yan; et al.
Abstract: Neuromorphic computing on the hardware level is promising for performing ever‐increasing data‐centric tasks owing to its superiority to conventional von Neumann architecture in terms of energy efficiency and learning ability. One key aspect to its implementation is the development of artificial synapses that can effectively emulate the multiple functionalities exhibited by their biological counterparts. Here, building on an inorganic ferroelectric gate stack integrated with a 2D layered semiconductor (WS2 ), a new type of ferroelectricity‐based synaptic transistor that differs from those relying on interface traps or floating gate configuration is reported. By virtue of a 6 nm thick ferroelectric hafnium zirconium oxide by atomic layer deposition and postannealing treatment, the device shows a channel resistance change ratio above 10 5 corresponding to opposite ferroelectric polarization direction. Furthermore, by applying electrical stimulus to the gate, it demonstrates good capability to mimic various synaptic behaviors including long‐term potentiation, long‐term depression, spike‐amplitude‐dependent plasticity, and spike‐rate‐dependent plasticity. Given the inherent compatibility of the ferroelectric gate stack with existing fabrication technology, and the reliability of ferroelectricity engineering, this work paves the way toward practical implementation of synaptic devices in neuromorphic circuits. Abstract : A synaptic transistor consisting of optimized ferroelectric Hf0.5 Zr0.5 O2 (HZO) and 2D tungsten disulfide (WS2 ) is reported to emulate biological functions. The as‐fabricated device efficiently mimics various synaptic behaviors, including long‐term potentiation, long‐term depression, spike‐amplitude‐dependent plasticity, and spike‐rate‐dependent plasticity, which manifests its potential for realizing energy‐efficient neuromorphic computing system.