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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reconfigurable Logic and in‐Memory Computing Based on Electrically Controlled Charge Trapping in Dielectric Engineered 2D Semiconductor Transistors

Zheng‐Dong Luo, Xuetao Gan, Fei Xue, Fei Xiao et al.
12 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
2.50
article

Reconfigurable Logic and in‐Memory Computing Based on Electrically Controlled Charge Trapping in Dielectric Engineered 2D Semiconductor Transistors

Zheng‐Dong Luo, Xuetao Gan, Fei Xue, Fei Xiao, Genquan Han, Yinshui Xia, Yan Liu, Dawei Zhang, Junpeng Zhang, Yue Hao, Dongxin Tan, Qiyu Yang, Zhufei Chu
article en
12 citations

Abstract

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.

Advanced Functional MaterialsVol. 35(13)
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)
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
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
2.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.