Integrated In‐Memory Sensor and Computing of Artificial Vision Based on Full‐vdW Optoelectronic Ferroelectric Field‐Effect Transistor

TLDR

Artificial intelligence drives the need for low‑power, compact systems that integrate sensing, memory, and neuromorphic computing, and 2D van der Waals materials enable continued device downscaling through arbitrary stacking. The study designs light‑induced ferroelectric Fe‑FETs to achieve high‑performance memory and multi‑functional sensing‑memory‑computing vision simulations. The authors fabricate 2D SnS₂/h‑BN/CuInP₂S₆ ferroelectric field‑effect transistors and employ light‑induced ferroelectric polarization reversal to realize high‑performance memory and multi‑functional sensing‑memory‑computing vision simulations. The device achieves an on/off ratio >10⁵, retention >10⁴ s, endurance >350 cycles, 128 multilevel states, emulates synaptic plasticity, attains 93.62 % MNIST accuracy, and mimics retina‑like adaptation and Pavlovian conditioning, demonstrating a strategy for multilevel memory and neuromorphic vision systems.

Abstract

Abstract The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS 2 /h‐BN/CuInP 2 S 6 (CIPS)‐based ferroelectric field‐effect transistors (Fe‐FETs) and utilized light‐induced ferroelectric polarization reversal to achieve excellent memory properties and multi‐functional sensing‐memory‐computing vision simulations are designed. The device exhibits a high on/off current ratio of over 10 5 , long retention time (>10 4 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7‐bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), long‐term potentiation, and long‐term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina‐like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing‐memory‐processing.