Photoferroelectric All-van-der-Waals Heterostructure for Multimode Neuromorphic Ferroelectric Transistors

ACS Appl Mater Interfaces. 2023 Mar 29;15(12):15732-15744. doi: 10.1021/acsami.3c00092. Epub 2023 Mar 15.

Abstract

Interface-driven effects in ferroelectric van der Waals (vdW) heterostructures provide fresh opportunities in the search for alternative device architectures toward overcoming the von Neumann bottleneck. However, their implementation is still in its infancy, mostly by electrical control. It is of utmost interest to develop strategies for additional optical and multistate control in the quest for novel neuromorphic architectures. Here, we demonstrate the electrical and optical control of the ferroelectric polarization states of ferroelectric field effect transistors (FeFET). The FeFETs, fully made of ReS2/hBN/CuInP2S6 vdW materials, achieve an on/off ratio exceeding 107, a hysteresis memory window up to 7 V wide, and multiple remanent states with a lifetime exceeding 103 s. Moreover, the ferroelectric polarization of the CuInP2S6 (CIPS) layer can be controlled by photoexciting the vdW heterostructure. We perform wavelength-dependent studies, which allow for identifying two mechanisms at play in the optical control of the polarization: band-to-band photocarrier generation into the 2D semiconductor ReS2 and photovoltaic voltage into the 2D ferroelectric CIPS. Finally, heterosynaptic plasticity is demonstrated by operating our FeFET in three different synaptic modes: electrically stimulated, optically stimulated, and optically assisted synapse. Key synaptic functionalities are emulated including electrical long-term plasticity, optoelectrical plasticity, optical potentiation, and spike rate-dependent plasticity. The simulated artificial neural networks demonstrate an excellent accuracy level of 91% close to ideal-model synapses. These results provide a fresh background for future research on photoferroelectric vdW systems and put ferroelectric vdW heterostructures on the roadmap for the next neuromorphic computing architectures.

Keywords: ferroelectrics; neuromorphic computing; optoelectronics; synapse; two-dimensional materials.