Ultralow-Power Vertical Transistors for Multilevel Decoding Modes.

Zhang, Qing; Li, Enlong; Wang, Yongshuai; Gao, Changsong; Wang, Congyong; Li, Lin; Geng, Dechao; Chen, Huipeng et al. · Adv Mater · 2023

basic_science · Level V

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Abstract

Organic field-effect transistors with parallel transmission and learning functions are of interest in the development of brain-inspired neuromorphic computing. However, the poor performance and high power consumption are the two main issues limiting their practical applications. Herein, an ultralow-power vertical transistor is demonstrated based on transition-metal carbides/nitrides (MXene) and organic single crystal. The transistor exhibits a high J<sub>ON</sub> of 16.6 mA cm<sup>-2</sup> and a high J<sub>ON</sub> /J<sub>OFF</sub> ratio of 9.12 × 10<sup>5</sup> under an ultralow working voltage of -1 mV. Furthermore, it can successfully simulate the functions of biological synapse under electrical modulation along with consuming only 8.7 aJ of power per spike. It also permits multilevel information decoding modes with a significant gap between the readable time of professionals and nonprofessionals, producing a high signal-to-noise ratio up to 114.15 dB. This work encourages the use of vertical transistors and organic single crystal in decoding information and advances the development of low-power neuromorphic systems.