神经形态工程学
XNOR门
冯·诺依曼建筑
晶体管
计算机科学
CMOS芯片
材料科学
逻辑门
电子工程
计算机体系结构
纳米技术
拓扑(电路)
人工神经网络
电气工程
工程类
光电子学
人工智能
与非门
操作系统
电压
作者
Zheng‐Dong Luo,Siqing Zhang,Yan Liu,Dawei Zhang,Xuetao Gan,Jan Seidel,Yang Liu,Genquan Han,Marin Alexe,Yue Hao,Marin Alexe,Yue Hao
出处
期刊:ACS Nano
[American Chemical Society]
日期:2022-02-11
卷期号:16 (2): 3362-3372
被引量:101
标识
DOI:10.1021/acsnano.2c00079
摘要
In-memory computing featuring a radical departure from the von Neumann architecture is promising to substantially reduce the energy and time consumption for data-intensive computation. With the increasing challenges facing silicon complementary metal-oxide-semiconductor (CMOS) technology, developing in-memory computing hardware would require a different platform to deliver significantly enhanced functionalities at the material and device level. Here, we explore a dual-gate two-dimensional ferroelectric field-effect transistor (2D FeFET) as a basic device to form both nonvolatile logic gates and artificial synapses, addressing in-memory computing simultaneously in digital and analog spaces. Through diversifying the electrostatic behaviors in 2D transistors with the dual-ferroelectric-coupling effect, rich logic functionalities including linear (AND, OR) and nonlinear (XNOR) gates were obtained in unipolar (MoS2) and ambipolar (MoTe2) FeFETs. Combining both types of 2D FeFETs in a heterogeneous platform, an important computation circuit, i.e., a half-adder, was successfully constructed with an area-efficient two-transistor structure. Furthermore, with the same device structure, several key synaptic functions are shown at the device level, and an artificial neural network is simulated at the system level, manifesting its potential for neuromorphic computing. These findings highlight the prospects of dual-gate 2D FeFETs for the development of multifunctional in-memory computing hardware capable of both digital and analog computation.
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