Filled Carbon Nanotube Ternary Transistors

@s-SWCNT), in which carrier concentration can be dynamically modulated under an applied electric field. Transistors based on this platform validate that this electric-field-controlled modulation yields three well-defined logic states: 0, 1, and 2. These transistors demonstrate good uniformity and stable operation, showing a static power consumption of 8.2 pW and dynamic power consumption of 0.31 nJ (state 0 to 1) and 0.35 µJ (state 1 to 2). The ternary inverter based on this heterostructure exhibits rail-to-rail output capability, enabling the accurate execution of MVL operations. Ternary weight networks (TWNs) built with these transistors reduce computational complexity and storage, enabling efficient neuromorphic computing.

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Publication Details

Journal
Advanced Materials
Published
2026-04-03
DOI
https://doi.org/10.1002/adma.202523297
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Filled Carbon Nanotube Ternary Transistors

Mario Lanza, Zongjie Shen, Kunjie Wang, Jian Yao et al.
Advanced Materials
Advanced Memory and Neural Computing
article

Filled Carbon Nanotube Ternary Transistors

Mario Lanza, Zongjie Shen, Kunjie Wang, Jian Yao, Qinan Wang, Pin Zhao, Lin Geng, Chun Zhao, Yu Teng, Yanyan Zhao
article en

Abstract

@s-SWCNT), in which carrier concentration can be dynamically modulated under an applied electric field. Transistors based on this platform validate that this electric-field-controlled modulation yields three well-defined logic states: 0, 1, and 2. These transistors demonstrate good uniformity and stable operation, showing a static power consumption of 8.2 pW and dynamic power consumption of 0.31 nJ (state 0 to 1) and 0.35 µJ (state 1 to 2). The ternary inverter based on this heterostructure exhibits rail-to-rail output capability, enabling the accurate execution of MVL operations. Ternary weight networks (TWNs) built with these transistors reduce computational complexity and storage, enabling efficient neuromorphic computing.

Advanced MaterialsVol. 38(25)
University of Science and Technology of China (CN), National University of Singapore (SG), Suzhou Institute of Nano-tech and Nano-bionics (CN), Xi’an Jiaotong-Liverpool University (CN)
National Research Foundation Singapore, National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 36%
Advanced Memory and Neural Computing
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