Reconfigurable Logic and in‐Memory Computing Based on Electrically Controlled Charge Trapping in Dielectric Engineered 2D Semiconductor Transistors
Abstract The co‐integration of logic, memory, synapse, and other essential functionalities into one single element with run‐time reconfigurability is explored as a promising approach for an efficient and flexible in‐memory computing platform. However, despite ample research focused on such reconfigurable semiconductor technology, it remains challenging to achieve a CMOS‐compatible device concept that is with simplified device structure, versatile functionalities, and efficient operation schemes. Here, a new type of run‐time electrically reconfigurable device is demonstrated based on dielectric‐engineered 2D semiconductor transistors. With an engineered semiconductor/charge‐trap layer/dielectric film heterostructure, the 2D charge‐trap transistor (CTT) resembles a simplified metal‐oxide‐semiconductor field‐effect transistor (MOSFET) structure. Both multilevel permanent charge trapping and transient voltage‐modulating capabilities can be realized in the 2D CTTs, giving rise to various switchable device function modes including non‐volatile memory, threshold voltage‐variable logic switch, and artificial synapse. Leveraging the monolithic integration of multiple 2D CTTs and time‐sequential reconfigurable operation strategy, high‐performance logic inverter and non‐volatile ternary content‐addressable memory (TCAM) with compact architecture can be created. The performance of the 2D CTTs in synapse mode is evaluated with the simulation of the convolutional neural network, showing great potential for future neuromorphic computing hardware.
Authors
- Zheng‐Dong Luo (ORCID: https://orcid.org/0000-0003-3725-4912)
- Xuetao Gan (ORCID: https://orcid.org/0000-0003-2469-5807)
- Fei Xue (ORCID: https://orcid.org/0000-0001-9039-7088)
- Fei Xiao (ORCID: https://orcid.org/0000-0001-7353-2700)
- Genquan Han (ORCID: https://orcid.org/0000-0001-5140-4150)
- Yinshui Xia (ORCID: https://orcid.org/0000-0002-3831-3876)
- Yan Liu (ORCID: https://orcid.org/0000-0001-5583-0587)
- Dawei Zhang (ORCID: https://orcid.org/0000-0001-5096-2046)
- Junpeng Zhang (ORCID: https://orcid.org/0009-0008-9043-8212)
- Yue Hao (ORCID: https://orcid.org/0000-0002-8081-2919)
- Dongxin Tan
- Qiyu Yang (ORCID: https://orcid.org/0009-0000-9704-4170)
- Zhufei Chu
Institutions
- Ningbo University (CN)
- Xuzhou University of Technology (CN)
- Xidian University (CN)
- Zhejiang University of Science and Technology (CN)
- Northwestern Polytechnical University (CN)
- UNSW Sydney (AU)
- ARC Centre of Excellence in Future Low-Energy Electronics Technologies (AU)
- Division of Materials Science and Engineering (AU)
- Zhejiang University (CN)
- Ministry of Industry and Information Technology (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1002/adfm.202417887
- Citations
- 12
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.50
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities
- Natural Science Foundation of Zhejiang Province