Multimodal 2D Ferroelectric Transistor with Integrated Perception-and-Computing-in-Memory Functions for Reservoir Computing

作者：Jiachao Zhou, Anzhe Chen, Yishu Zhang, Xinwei Zhang, Jian Chai, Jiayang Hu, Hanxi Li, Yang Xu, Xulang Liu, Ning Tan, Fei Xue, Bin Yu · 发表于：Nano Letters · 年份：2024 · DOI：10.1021/acs.nanolett.4c05071 · 被引用次数：22 · 研究领域：Neural Networks and Reservoir Computing、Advanced Memory and Neural Computing、Neural dynamics and brain function

Emerging neuromorphic hardware promises energy-efficient computing by colocating multiple essential functions at the individual component level. The implementation is challenging due to mismatch between the characteristics of multifunctional devices and neural networks. Here, we demonstrate an artificial synapse based on a 2D α-phase indium selenide that exhibits integrated perception-and-computing-in-memory functions in a single-transistor setup, serving as a basic building block for reservoir computing. Extending to the array architecture enables concurrent image-sensing and memory. Further, we implement multimode deep-reservoir computing with adjustable nonlinear transformation and multisensory fusion using this core device. In the lane-keeping-assistance task for an unmanned vehicle, the system demonstrates ∼10 4 times lower energy consumption and significantly boosted data throughput compared to the state-of-the-art graphics processors. The demonstrated perception-and-computing-in-memory (PCIM) functions at a single-transistor level shows the feasibility of implementing ultrascalable, resource-efficient hardware for brain-inspired computing.