Phase-Engineered In<sub>2</sub>Se<sub>3</sub> Ferroelectric P-N Junctions in Phototransistors for Ultra-Low Power and Multiscale Reservoir Computing
Jing‐Feng Li, Xiaoting Wang, Yang Ma, Wei Han, Kexin Li, Jingtao Li, Yi Wu, Yuehui Zhao, Tao Yan, Xiu Liu, Haolin Shi, Xiaoqing Chen, Yongzhe Zhang
Abstract
Two-dimensional (2D) ferroelectric field-effect transistors (Fe-FETs) based on p–n junctions are the basic units of future neuromorphic hardware. The In 2 Se 3 semiconductor with ferroelectric, photoelectric, and phase transition properties possesses great application potential for in-sensor computing, but its ferroelectric p–n junction (FePNJ) is not well investigated. Here, we present an optoelectronic synapse made of uniformly full-coverage α-In 2 Se 3 /WSe 2 FePNJ, achieving ultralow-power classification recognition and multiscale signal processing. Using chemical vapor deposition (CVD), we can obtain β′-In 2 Se 3 /WSe 2 subferroelectric p–n junctions by direct growth on SiO 2 /Si substrate and α-In 2 Se 3 /WSe 2 FePNJ by phase transition. Modulated by the synergistic effect of the polarization electric field and the built-in electric field, the FePNJ exhibits significantly enhanced and highly tunable synaptic effects (memory retention >2500 s and >8 multilevel current states under single optical/electrical pulses), along with power consumption down to atto-joule levels. Utilizing these photoelectric properties, we constructed an all-ferroelectric in-sensor reservoir computing system, comprising both reservoir and readout networks, achieving ultralow-power handwritten digit recognition. We also created a multiscale reservoir computing system through the gate-voltage-modulated relaxation time scale of the FePNJ, which can efficiently detect motions in the 1 to 100 km h –1 speed range.