Phase-Engineered In₂Se₃ Ferroelectric P-N Junctions in Phototransistors for Ultra-Low Power and Multiscale Reservoir Computing.

Abstract

Two-dimensional (2D) ferroelectric field-effect transistors (Fe-FETs) based on p-n junctions are the basic units of future neuromorphic hardware. The In₂Se₃ 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-In₂Se₃/WSe₂ FePNJ, achieving ultralow-power classification recognition and multiscale signal processing. Using chemical vapor deposition (CVD), we can obtain β'-In₂Se₃/WSe₂ subferroelectric p-n junctions by direct growth on SiO₂/Si substrate and α-In₂Se₃/WSe₂ 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.