Natl Sci Open
Volume 2, Number 4, 2023
Special Topic: Two-dimensional Materials and Devices
|Number of page(s)
|29 June 2023
2T1C DRAM based on semiconducting MoS2 and semimetallic graphene for in-memory computing
State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Zhangjiang Fudan International Innovation Center, Shanghai 200433, China
2 Shenzhen Six Carbon Technology, Shenzhen 518055, China
Revised: 20 March 2023
Accepted: 7 April 2023
In-memory computing is an alternative method to effectively accelerate the massive data-computing tasks of artificial intelligence (AI) and break the memory wall. In this work, we propose a 2T1C DRAM structure for in-memory computing. It integrates a monolayer graphene transistor, a monolayer MoS2 transistor, and a capacitor in a two-transistor-one-capacitor (2T1C) configuration. In this structure, the storage node is in a similar position to that of one-transistor-one-capacitor (1T1C) dynamic random-access memory (DRAM), while an additional graphene transistor is used to accomplish the non-destructive readout of the stored information. Furthermore, the ultralow leakage current of the MoS2 transistor enables the storage of multi-level voltages on the capacitor with a long retention time. The stored charges can effectually tune the channel conductance of the graphene transistor due to its excellent linearity so that linear analog multiplication can be realized. Because of the almost unlimited cycling endurance of DRAM, our 2T1C DRAM has great potential for in situ training and recognition, which can significantly improve the recognition accuracy of neural networks.
Key words: molybdenum disulfide (MoS2) / graphene / DRAM / in-memory computing
© The Author(s) 2023. Published by China Science Publishing & Media Ltd. and EDP Sciences.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.