A Multifunctional Biomimetic Flexible Sensor Based Novel Artificial Tactile Neuron with Perceptual Memory

Abstract

to provide us with awareness and guidance. [1] Biological systems inherently run on a distributed computing paradigm with superior fault tolerance and power efficiency inherent to adaptive, plastic and event-driven sensory neuron networks. [2,3] Thus, emulating biological behavior from sensory neurons is a fundamental implementation of bio-sensory capabilities. [4] There are three key components in the biologic sensory system containing receptor cells, nerve channels and sensory-related parts of the brain, as shown in Figure 1. The biologic tactile nervous system is one of the ways in which external information is acquired. Skin (sensory receptor) perceive external stimuli and presynaptic neurons generate action potentials to release neurotransmitters that trigger postsynaptic neurons, and the electrical signal then propagates along the afferent neurons to the somatosensory cortex. [5] Accordingly, the artificial sensory memory (ASM) device also should contain three core components (see Figure 1b): the E-skin (artificial receptor) to convert external physical stimuli into electrical signal pulses, the artificial synapse to integrate the signals and convert signals into excitatory postsynaptic current (EPSC), and the artificial axon to connect them.Recently, there appears some significant breakthroughs in combining advanced bio-inspired sensing and neuromorphic engineering technologies. [6][7][8][9][10] In particular, different neuromorphic systems based on field-effect transistor (FET)based synaptic devices have been developed for tactile simulation. [3,11,12] For example, Zhu et al. used piezoresistive sensors to convert pressure stimuli into electrical signals, which were transmitted to indium-tungsten-oxide synaptic transistors through soft ion conductors; the system can integrate and extract tactile pattern features for pattern recognition. [13] Wan et al. proposed a bio-inspired energy-saving tactile sensor by combining the triboelectric nanogenerator and IZGO-based synaptic transistor compatible. [11] Triboelectric nanogenerator converts dynamic pressure pulse into gate voltage pulse to adjust the channel conduction of synaptic transistor. However, with the development of integrated circuit miniaturization technology, transistor cannot effectively retain electrons and store information. Recently, the emerging nanoelectronics synaptic devices are drawing more attention due to their superior properties.The bio-inspired sensory memory device functions as a multifunctional artificial perceptual learning system that can effectively analyze and retain multiple sensory signals. In this work, the multiscale hierarchical surface of reed leaves is templated to prepare CNTs/PDMS films, and a new type of electronic skin (E-skin) is constructed. Due to its unique multi-scale sensing layer surface, E-skin exhibits a wide pressure detection range and ultrahigh sensitivity (235.95 kPa −1 ) and linearity (0.99 under 1.95-12.4 kPa). The memristor serving as artificial synapse is formed with HfO 2 film as resistive switch lay...

Memristor‐Based Intelligent Human‐Like Neural Computing

“…In response to varying degrees of external stimuli, memristors respond differently due to their threshold switching behaviors and richly dynamic properties. [ 215,216 ] As the stimulus reached a certain threshold, the state of the memristor changed. Zhang et al.…”

“…While the CNT/PDMS sensor transforms the pressure information into electrical signals, the memristor serves as a synapse, and the pressure information can be retained in the memristor element. [ 215 ]…”

“…While the CNT/PDMS sensor transforms the pressure information into electrical signals, the memristor serves as a synapse, and the pressure information can be retained in the memristor element. [215] In terms of visual perception systems, memristors can map external image information to its conductance. Similar to the tactile perception system, the visual perception systems can be demonstrated by connecting light-sensitive devices to ordinary memristors.…”

Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm

“…With its good flexibility and stretchability, it can be attached to the surface of irregular objects, and it is widely used in the research of robotic bionic skin [ 6 , 7 , 8 , 9 ]. Therefore, researchers improve the performance of sensors from the aspects of materials [ 10 , 11 , 12 ], multifunctional integration [ 13 , 14 , 15 ], and self-energy [ 16 , 17 , 18 ]. At present, flexible tactile sensors based on capacitive [ 19 ], piezoresistive [ 20 , 21 ], piezoelectric [ 22 , 23 ], and other mechanisms have been continuously proposed, and they have shown good application prospects.…”

Neuromorphic metamaterials for mechanosensing and perceptual associative learning

“…Spatiotemporal, long-term memory formation and retention has also been shown using a 3x3 array of CNT/PDMS piezoresistive sensors and Pt/HfO 2 /TiN memristors. 24 Online learning (implying adaptation as data is sensed) is a crucial function of neuromechanical systems that has so far been much less explored in mechanically reconfigurable, neuromorphic material systems. Thus, synthetic material systems enabling integrated mechanosensing, memory, and autonomic learning capabilities remain rare.…”