Ultralow‐Power Vertical Transistors for Multilevel Decoding Modes

Abstract

addressing probabilistic, and unstructured problems, thus is anticipated to solve the von Neumann bottleneck and become the mainstay of contemporary computing systems. [1,2] Synapses serve a crucial function in signal transmission in the biological neural system. [3] They transmit signals through neurotransmitters in a manner similar to that of field-effect transistors, where source-drain current changing in response to changes in gate voltage. In addition, organic materials are solutionprocessable, [4][5][6][7] thus simulating the functions of biological synapses by organic field-effect transistors has received intensive attention. [8,9] Most recently, higher performance requirements for devices are put forward to support practical applications, including fast switching speed, low energy consumption, low operating voltage, and strong compatibility with flexible substrate. Traditional planar field effect transistors struggle to accomplish these functions. Conversely, vertical organic field-effect transistors (VOFETs) have a more stable sourcedrain current and a changeable channel length that depends 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 ultralowpower vertical transistor is demonstrated based on transition-metal carbides/ nitrides (MXene) and organic single crystal. The transistor exhibits a high J ON of 16.6 mA cm −2 and a high J ON /J OFF ratio of 9.12 × 10 5 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.

Brain-like optoelectronic artificial synapses with ultralow energy consumption based on MXene floating-gates for emotion recognition

“…2g, which indicates a lower energy consumption of this work, even lower than that of the biological system (10 fJ per synaptic event). 25,[43][44][45][46][47][48][49][50][51][52][53][54] The result indicated that the device in this work has great potential in simulating the behaviors and energy consumption of human brain synapses.…”

Bioinspired Vertical Transistors from Artificial Synapse to Neuromorphic System

“…A summary for the synaptic vertical transistors based on different materials. [66,94,95,104,[107][108][109][110][111] Reproduced with permission. [66] Copyright 2021, John Wiley and Sons.…”

“…To overcome this drawback, Chen et al constructed a high‐performance vertical transistor with an ultralow power consumption using MXene (transition metal carbides/nitrides) to realize the multilevel information decoding strategies. [ 104 ] Figure a depicts the preparing process of vertical MXene‐DPA transistor. The information decoding ability of VOFET is explored after evaluating its electrical and optoelectric performances.…”

“…Reproduced with permission. [ 104 ] Copyright 2022, John Wiley and Sons. c) Schematic illustration the structure of TENG, vertical transistor, and VTT, respectively.…”

“…Reproduced with permission. [ 104 ] Copyright 2022, John Wiley and Sons. Reproduced with permission.…”