With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human–machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Therefore, low-dimensional materials, including both 0-dimensional materials (0D), one-dimensional (1D) materials, and two-dimensional (2D), have been employed in flexible neuromorphic devices and systems.
1. Introduction
With the development of information technology and intelligent systems, the amount of data will double every two years, and these data need to be processed with high efficiency. However, the memory wall in conventional Von Neumann-based computing systems will limit their energy efficiency and their computing performance, due to the frequent transmission of data between memory and the processing unit
[1,2][1][2]. In contrast, benefiting from large-scale parallel processing and event-driven operation of the biological neural network, the human brain is able to work in robust, fault tolerant, and energy-efficient modes
[3[3][4][5],
4,5], and only consumes about 20 W of power. In addition, some level activities, such as reflex and muscle activation, are processed directly by the peripheral neural system (PNS) without sending signals to the central neural system (CNS). That is to say, the decisions responding to some sensory signals can be made locally and immediately, once the sensory signals are perceived
[6]. Localized processing can not only quickly respond to external stimuli and protect living creatures from further injury, but also reduce the computational burden of the brain
[6,7][6][7]. This is considered a potential approach for designing and constructing a neuromorphic system by mimicking the structure and mechanism of its biological counterpart, which can realize both the biological neural-sensing and processing functions
[8,9,10][8][9][10]. Therefore, in-memory computing and in-sensor computing have been proposed and the corresponding neuromorphic devices have been fabricated
[11,12,13][11][12][13].
The emerging applications of neuromorphic systems, such as healthcare monitoring, neuro-prosthetics, and human–machine interfaces, require the flexibility of neuromorphic devices and systems in the morphology, which is highly related to the mechanical properties of the materials. Usually, bulk inorganic materials are rigid and incompatible with flexible electronics. In order to realize the high flexibility of the devices, several approaches have been proposed. The employment of organics, such as biomaterials and polymers, in the fabrication of the devices is one of the possible solutions
[14,15,16,17,18][14][15][16][17][18]. Although devices with high flexibility can be obtained with organic materials, environmental sensitivity (e.g., temperature, moisture, and chemicals) is an issue influencing the long-term stability of the devices
[19,20][19][20]. Inorganic materials also show high flexibility when the thickness of the materials is scaled to several micrometers and can be conformable with human skin, as reported in previous research. The use of inorganic materials is also a possible approach to fabricating flexible electronics
[21,22][21][22].
Therefore, low-dimensional materials, including both 0-dimensional materials (0D), one-dimensional (1D) materials, and two-dimensional (2D), have been employed in flexible neuromorphic devices and systems
[23,24,25,26,27][23][24][25][26][27]. In addition, some low-dimensional materials, such as transition-metal dichalcogenide (TMDs), graphene, and nanotubes, exhibit properties that are sensitive to external stimuli, which is also required for sensing applications. In addition, power consumption is also an issue that needs to be considered in flexible electronics for neuromorphic sensing applications. Thus, a number of self-powered flexible sensers were also designed and fabricated
[28,29,30,31][28][29][30][31].
Therefore, energy efficient in-sensor computing devices, fabricated with low-dimensional materials, show great potential applications in neuromorphic engineering
[32,33,34][32][33][34].
2. Low-Dimensional Materials
With the development of material science and processing technology, several novel materials with distinct electrical and physical properties have been discovered and synthesized. Among them, low-dimensional materials with at least one dimension at the nanoscale level, have distinct properties from their corresponding bulk materials
[35]. Due to their outstanding properties, low-dimensional materials have attracted a great amount of attention
[36,37,38,39,40][36][37][38][39][40]. For example, 2D materials, such as transitional metal dichalcogenides (TMDs), 2D oxides, and MXene, and 1D materials, such as carbon nanotubes, metal oxide nanotubes, and silicon nanowires, have been synthesized and widely applied. In addition, 0D materials, such as quantum dots and nanoparticles, were used in the dielectric layer of a memristive device
[26,27,41][26][27][41]. These materials show superior flexibility for the implementation of flexible electronics than their bulk counterparts. Mechanical flexibility allows the devices to stably adhere on the surfaces of an arbitrary object, including human skin and organs, as shown in
Figure 1.
[42]. In addition, the electrical properties of low-dimensional materials range from insulators to conductors, and a number of flexible electronics, ranging from flexible electrodes to heterostrcure devices consisting of both insulators and conductors, can be fabricated with low-dimensional materials. Furthermore, the properties can be tuned by various methods, which will further contribute to the various applications of low-dimensional materials. For example, the electrical and physical properties of 2D materials can be tuned by adjusting the layer or doping with other elements
[43]. For1D and 0D materials, the modification of the surface morphology and the functional groups can also change the properties
[44,45][44][45]. In addition, size, diameter, and surface roughness will also lead to a variety of electrical and physical properties of low-dimensional materials
[46,47][46][47].
Figure 1.
The application of low-dimensional materials in flexible electronics.
Hence, this section will provide a brief introduction to low-dimensional materials and their applications in flexible electronics.
Before discussing low-dimensional materials and their artificial synapses applications in detail,
Table 1 provides a brief summary of the advantages and disadvantages of low-dimensional materials.
Table 1.
Summary of the advantages and disadvantages of low-dimensional materials in the applications of flexible artificial synapses.