In-plane ferroelectric-reconfigured interface towards dual-modal intelligent vision

Yichen Cai , Yizhou Jiang , Xiaofei Yue , Chenxu Sheng , Yajie Qin , Shisheng Xiong , Yiqiang Zhan , Zhi-Jun Qiu , Ran Liu , Wei Chen , Zheng Liu , Laigui Hu , Chunxiao Cong
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Abstract

With the rapid progress of intelligent vision of the Internet of Things, the gap between the resultant high-level demands and conventional silicon-based hardware architectures is continuously widening. Biomimetic artificial neural networks (ANNs) have recently been proposed to solve this problem. However, they still face the predicaments in uniformity, heterogeneous integration, and hardware implementation, etc. Here we demonstrate an intelligent vision ANN with two-dimensional material (2DM)/molecular ferroelectric (MF) bilayer electronic/optoelectronic memristors. In contrast to conventional ferroelectric transistors, in-plane ferroelectric polarization was also found to enable a simpler two-terminal structure with a lateral p/n-type doping in the adjacent 2DM layer. After a demonstration of fabricated array and board-level driving circuits, an image recognition and classification task is proposed, reaching an accuracy of 85.2%, which implies great potential towards multi-modal artificial intelligent vision systems.

面向双模式智能视觉的平面内铁电重构界面
随着物联网智能愿景的快速发展,由此产生的高层次需求与传统硅基硬件架构之间的差距正在不断扩大。最近,仿生人工神经网络(ANN)被提出来解决这一问题。然而,它们仍然面临着统一性、异构集成和硬件实现等方面的困境。在此,我们展示了一种采用二维材料(2DM)/分子铁电(MF)双层电子/光电忆阻器的智能视觉神经网络。与传统的铁电晶体管相比,我们发现平面内的铁电极化也能使相邻 2DM 层中的横向 p/n 型掺杂的双端结构变得更简单。在演示了制造的阵列和板级驱动电路后,提出了一项图像识别和分类任务,准确率达到 85.2%,这意味着多模态人工智能视觉系统具有巨大潜力。
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