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

作者：Peng Wang, Jie Li, Wuhong Xue, Wenjuan Ci, Feng-Xian Jiang, Lei Shi, Feichi Zhou, Peng Zhou, Xiaohong Xu · 发表于：Advanced Science · 年份：2023 · DOI：10.1002/advs.202305679 · 被引用次数：111 · 研究领域：Advanced Memory and Neural Computing、Perovskite Materials and Applications、Advanced Sensor and Energy Harvesting Materials

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 develop...