Fast and robust multilevel optoelectronic memory based on van der Waals heterostructure

Author:

Liu Tao1ORCID,Wang Yue2,Cao Yi2,Tan Feixia2,Li Honghong2,Wang Tinghao2,Xiang Du23ORCID

Affiliation:

1. Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics and Department of Materials Science, Fudan University 1 , Shanghai 200433, China

2. State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University 2 , Shanghai 200433, China

3. Zhangjiang Fudan International Innovation Center, Fudan University 3 , Shanghai 200433, China

Abstract

Optoelectronic memory (OEM) has attracted tremendous attention for its great potential to boost the storage capacity of memory chips and break through the von Neumann bottleneck in the post-Moore era. Two-dimensional (2D) van der Waals (vdW) heterostructures, formed by artificially stacking different 2D layered materials, offer tremendous possibilities in OEMs due to their extraordinary capability to integrate and process optical/electrical signals. However, the realization of 2D vdW OEMs with high writing speed and robust memory performance has long been challenging. Here, we report a 2D vdW OEM consisting of tungsten diselenide (WSe2) and hexagonal boron nitride, which functions based on the fast charge transfer dynamics at a 2D interface. The OEM demonstrates high writing speed reaching up to 50 μs, approximately one order of magnitude faster than those of other 2D OEMs. Moreover, the outstanding robustness of such OEM is demonstrated by long retention time exceeding 14 days, together with a broad temperature endurance window from 100 to 420 K. Additionally, through continuously switching laser pulse on the OEM, we achieve 17 distinct current levels (over 4-bit storage) with random access. Our findings envision 2D vdW heterostructure-based OEM as a potential platform to overcome the “memory wall” in the conventional von Neumann configuration and to promote a promising paradigm for big data storage.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Shanghai Rising-Star Program

Shanghai Sailing Program

National Key R&D Program of China

Publisher

AIP Publishing

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