J. Semicond. > 2025, Volume 46 > Issue 1 > 011606
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REVIEWS

Artificial sensory neurons and their applications

Jiale Shao1, 2, Hongwei Ying3, Peihong Cheng2, 3, Lingxiang Hu2, Xianhua Wei4, Zongxiao Li2, , Huanming Lu2, , Zhizhen Ye5, 6 and Fei Zhuge2, 5, 7,

+ Author Affiliations

 Corresponding author: Zongxiao Li, lzx_1102@163.com; Huanming Lu, hmlu@nimte.ac.cn; Fei Zhuge, zhugefei@nimte.ac.cn

DOI: 10.1088/1674-4926/24080039CSTR: 32376.14.1674-4926.24080039

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Abstract: With the rapid development of artificial intelligence (AI) technology, the demand for high-performance and energy-efficient computing is increasingly growing. The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution. Neuromorphic computing mimics the working principles of the human brain, characterized by high efficiency, low energy consumption, and strong fault tolerance, providing a hardware foundation for the development of new generation AI technology. Artificial neurons and synapses are the two core components of neuromorphic computing systems. Artificial perception is a crucial aspect of neuromorphic computing, where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research. This work reviews recent advances in artificial sensory neurons and their applications. First, biological sensory neurons are briefly described. Then, different types of artificial neurons, such as transistor neurons and memristive neurons, are discussed in detail, focusing on their device structures and working mechanisms. Next, the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated, covering various sensory types, including vision, touch, hearing, taste, and smell. Finally, challenges faced by artificial sensory neurons at both device and system levels are summarized.

Key words: artificial sensory neurons, artificial perception systems, neuromorphic computing, artificial intelligence



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Fig. 1.  (Color online) Schematic structure of this review's specific contents[30−39].

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Fig. 2.  (Color online) Schematic diagram of sensory nerve systems in the human brain[40].

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Fig. 3.  (Color online) Emerging silicon neurons based on latch-up effect. (a) A 1T neuron fabricated by silicon nanowire; (b) energy band diagrams of the 1T neuron device for the excitatory function (left and middle) and the inhibitory function (right); (c) real time spiking characteristics modulated by the gate voltage (VG) pulse in which the 1T neuron is significantly suppressed when the VG is 1 V[33].

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Fig. 4.  (Color online) (a) 3D device structure of sub-10 nm vertical ITO transistor[47]; (b) 3D device structure of sub-10 nm vertical coplanar-multiterminal flexible transient ITO phototransistor network[48].

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Fig. 5.  (Color online) (a) Typical I−V curve of a NbOx-based TS memristor; (b) schematic diagram of artificial spiking neuron circuit based on NbOx-based memristor; (c) oscillation and output spiking characteristics of memristive neuron under constant voltage stimuli[54]; (d) summary of effect of TS device performance on neuron characteristics[66]; (e) TS characteristics before and after the RESET operation with a cutoff voltage of −3 V; (f) Vth variation tendency upon different RESET voltages[36].

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Fig. 6.  (Color online) (a) LIF neurons with frequency-adaptive firing functions[69]; (b) the volatile typical transfer curves (up) of an antiferroelectric field effect transistor (AFeFET) and the continuous firing events (down) of an AFeFET neuron under voltage pulses; (c) the dynamic of leaky and integration process of AFeFET neuron under gate pulse with different amplitudes[30]; (d) schematic of an antiferromagnetic spintronic device; (e) schematic of a polar magneto-optic Kerr effect microscope setup for in-situ magneto-electrical transport probing (up) and the measured domain wall position of hall bar under current stimuli (down); (f) domain wall position signal (up), neural threshold signal (middle) and the output voltage spike dynamics (down) of the antiferromagnetic spintronic device under current stimuli, the inset presents dynamics of domain wall motion[38].

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Fig. 7.  (Color online) (a) Schematic diagram of human visual system (up) and an artificial visual neuron composed of a UV sensor and an oscillation neuron (down); (b) the output oscillatory spikes of the artificial visual neuron under simultaneous UV irradiation of 254 and 365 nm; (c) the segmentation result of UV image based on the artificial visual system[89]; (d) an artificial visual perception system based on a filament-based TS neuron; (e) evolution of parameters of the artificial visual perception system during the car passing process[35]; (f) schematic illustration of the eye self-adaptation to near and distant vision; (g) circuit scheme of an artificial visual neuron for near and distant vision stimulation; (h) the relationship between firing rate and distance of the artificial visual neuron[90].

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Fig. 8.  (Color online) Optoelectronic neuron devices. (a) TEM image of an optoelectronic neuron based on silicon transistor; (b) spiking characteristics of the optoelectronic neuron based on silicon transistor under red light stimuli with different intensities; (c) spiking frequency change of the silicon neuron under various intensities of red, blue, and green light stimuli[95]; (d) an artificial eye based on FLBP/CsPbBr3-based TS memristor; (e) a collision detection system based on the artificial eye; (f) decision-making for a robot car with optic signal processing ability[97]; (g) schematic structure of a MoS2-based optoelectronic graded neuron; (h) photocurrent changes of the optoelectronic graded neuron with time under single light pulse stimuli; (i) motion direction recognition with high-efficiency based on the optoelectronic graded neuron[99]; (j) schematic diagram of polarization-sensitive photodetection system based on ReS2 phototransistor; (k) transmission characteristics of ReS2 phototransistor at different polarization angles[100].

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Fig. 9.  (Color online) (a) Illustration of an artificial tactile neuron consisting of a NbOx-based memristor and a piezoelectric sensor; (b) the neural firing dynamics of the artificial tactile neuron under pressure; (c) the self-protective behavior of the artificial tactile neuron under high pressure[102]; (d) an artificial tactile neuron composed of a resistive pressure sensor with micro-pyramid and NbOx-based memristor; (e) the spiking frequency mapping of the artificial tactile neuron array under handprint pressure; (f) the output grayscale image of handprint after the PCNN processing[103]; (g) the output currents at a different input voltage amplitudes in log scale (up) and in linear scale (down) and emulation of allodynia and hyperalgesia phenomena of nociceptor; (h) the input voltage curves (Ch1) and the output voltage curves (Ch2) of an artificial thermal nociceptor[104].

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Fig. 10.  (Color online) An artificial auditory perception system based on artificial neuron. (a) Schematic of biological auditory system; (b) schematic of an artificial auditory perception system based on silicon biristor neuron; (c) circuit of an artificial auditory perception system for pitch classification; (d) the synaptic currents of the artificial auditory perception system after applying various G3 type music[111].

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Fig. 11.  (Color online) Artificial gustatory perception systems based on artificial neurons. (a) Schematic of biological gustatory system; (b) schematic of artificial gustatory system based on a 1T neuron and a gustatory sensor; (c) the principle of the pH sensor response to hydrogen ions; (d) output characteristics of the 1T neuron under different pH levels; (e) spiking characteristics of the fabricated artificial gustatory neuron under different pH levels; (f) schematic of an artificial gustatory perception system composed of hydron/sodion sensitive sensors and 1T neurons; (g) synapse currents measured at output layer A (Isyn,A) when vinegar is applied to the gustatory system; (h) synapse currents of output layer B (Isyn,B) when brine is applied to the gustatory system[112].

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Fig. 12.  (Color online) An artificial olfactory perception system based on artificial neuron. (a) Schematic of biological olfactory system; (b) an artificial olfactory neuron composed of a 1T neuron and a gas sensor based on semiconductor metal oxide; (c) SEM image of the 1T neuron; (d) measurement scheme for the 1T neuron operation; (e) spiking characteristics of the 1T-neuron under 100 and 400 nA stimuli; (f) dynamic responses of the SnO2 and WO3 gas sensors to NH3 gas; (g) spiking characteristics of the artificial olfactory neuron composed of SnO2 gas sensor and 1T-neuron to NH3 with various parts per million (ppm); (h) synapse currents collected at the output layer of the artificial olfactory perception system after applying Merlot wine; (i) synapse currents of the artificial olfactory perception system after applying Shiraz wine[32].

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Fig. 13.  (Color online) Tactile pressure and temperature multi-modal fusion perception. (a) Schematic of multi-modal perception neurons based on the MFSN array; (b) I−V curve of the NbOx threshold resistive memristor; (c) schematic circuit diagram of the MFSN; (d) characteristics of the output spike under different pressures; (e) output spike characteristics of the MFSN with a constant input voltage of 4 V at temperatures of 20, 40, and 60 °C[120].

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Fig. 14.  (Color online) (a) Schematic of the bimodal artificial sensory neuron with visual-haptic fusion. Sub-figures ⅰ to ⅳ: photodetector, pressure sensor, hydrogel (dyed by 0.04% methylene blue), and synaptic transistor, respectively; (b) schematic of the relative change of ΔEPSC% over time interval (ΔT); (c) schematic of visual-haptic fusion for muscle actuation; (d) different responses of skeletal muscle tube to average velocity at different ΔEPSC% changes[119]; (e) schematic of a biologically inspired multi-sensory neural network; (f) schematic diagram of visual-tactile fusion perception, which illustrates the human ability to recognize and visualize tactile input; (g) visual−auditory fusion in which the number of PSC pulses is used to simulate how close the car is to the person[37].

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Table 1.   Performance comparison of sensory neurons.

Sense type Sensor device Neuron device Neuron type Input voltage/
current		 Max. frequency Perceptible signal type Ref.
Vision Ta/InGaZnO4/Pt NbOx memristor Oscillating 3 V 17.5 MHz UV light [89]
TiN/PbS/ITO MoS2/MoOx memristor Oscillating/
LIF		 1 V 160 Hz Red/green/blue light [35]
Photoresistor TaOx memristor LIF 5 V 200 Hz Green light [90]
− Silicon transistor Oscillating 100 nA 600 Hz Red/green/blue light [95]
− InGaAs n+/p/n+ transistor Oscillating 1 mA 41.5 kHz Red/green/blue/
infrared light		 [96]
− Black phosphorus/
CsPbBr3 memristor		 LIF 2 V 2.5 Hz Red/green/blue light [97]
− IGZO/Ag/Ta2O5/
memristor		 Oscillating 0.5 V 1200 Hz UV/red/green light [98]
− MoS2 transistor − Vds 0.1 V
Vg 3 V		 100 Hz Red light [99]
Touch Piezoelectric NbOx memristor Oscillating Analog type 1.1 MHz Pressure [102]
Piezoresistance NbOx memristor Oscillating −3 V 3.83 MHz Pressure [103]
Triboelectricity Silicon transistor Oscillating 100−200 nA 2.5 kHz Pressure [105]
Piezoresistance Sn13Ge37Se50 transistor Oscillating 6 V 1.2 MHz Pressure [104]
− Ag:SiO2 memristor LIF 0.6 V − Harm [107]
− TaOx memristor LIF 6 V − Harm [108]
− MAPbI3 memristor LIF 1.7 V − Harm [109]
Hearing Piezoelectric CMOS transistor FHN − − Voice [110]
Triboelectricity Silicon transistor Oscillating 100 nA 196 Hz Voice [111]
Taste pH value sensor, Na+ sensor Silicon transistor Oscillating 20 nA 1.2 kHz Sour, salty [113]
Smell SnO2, WO3 Silicon transistor Oscillating 100 nA 600 Hz NH3, CO2, NO2, acetone [32]
Multimodal Piezoresistance, NbOx NbOx memristor Oscillating 5 V 1.2 MHz Pressure, temperature [120]
Photodetector, pressure sensor Silicon transistor LIF 3 V − Red light, pressure [119]
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    Received: 25 August 2024 Revised: 02 October 2024 Online: Accepted Manuscript: 04 November 2024Uncorrected proof: 13 December 2024Published: 15 January 2025

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      Article Navigation >  Journal of Semiconductors  > 2025  >  46(1): 011606
      Jiale Shao, Hongwei Ying, Peihong Cheng, Lingxiang Hu, Xianhua Wei, Zongxiao Li, Huanming Lu, Zhizhen Ye, Fei Zhuge. Artificial sensory neurons and their applications[J]. Journal of Semiconductors, 2025, 46(1): 011606. doi: 10.1088/1674-4926/24080039 ****J L Shao, H W Ying, P H Cheng, L X Hu, X H Wei, Z X Li, H M Lu, Z Z Ye, and F Zhuge, Artificial sensory neurons and their applications[J]. J. Semicond., 2025, 46(1), 011606 doi: 10.1088/1674-4926/24080039
      Citation:
      Jiale Shao, Hongwei Ying, Peihong Cheng, Lingxiang Hu, Xianhua Wei, Zongxiao Li, Huanming Lu, Zhizhen Ye, Fei Zhuge. Artificial sensory neurons and their applications[J]. Journal of Semiconductors, 2025, 46(1): 011606. doi: 10.1088/1674-4926/24080039 ****
      J L Shao, H W Ying, P H Cheng, L X Hu, X H Wei, Z X Li, H M Lu, Z Z Ye, and F Zhuge, Artificial sensory neurons and their applications[J]. J. Semicond., 2025, 46(1), 011606 doi: 10.1088/1674-4926/24080039
      shu

      Artificial sensory neurons and their applications

      DOI: 10.1088/1674-4926/24080039
      CSTR: 32376.14.1674-4926.24080039
      • 1.

        Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

      • 2.

        Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China

      • 3.

        School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China

      • 4.

        State Key Laboratory of Environment-friendly Energy Materials, Southwest University of Science and Technology, Mianyang 621010, China

      • 5.

        Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China

      • 6.

        State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China

      • 7.

        Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200072, China

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      • Jiale Shao is studying for a master's degree in optoelectronic information engineering at Ningbo University, Ningbo, China. His M.S. study was under the Joint Education Program between Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences and Ningbo University. His research interests focus on oxide-based optoelectronic memristive devices and their applications in brain-inspired computing
      • Zongxiao Li got his PhD degree in 2016 at Harbin Institute of Technology, then joined Shenzhen university as assistant professor. Since October 2020, He joined Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences as a postdoc. His research interests include brain-inspired electronic devices, such as artificial synapse, neuron
      • Huanming Lu is currently a professor in Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences. His research interests are mainly on the microstructure of new materials such as semiconductor materials, magnetic materials and energy storage materials
      • Fei Zhuge received his Ph.D. degree in materials science from Zhejiang University, China, in 2005. He then became a JSPS postdoctoral fellow in Hiroshima University, Hiroshima, Japan. He joined Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS) in 2008. Since 2014, he has been a professor at NIMTE. Since 2018, he has been a Guest Professor at Center for Excellence in Brain Science and Intelligence Technology, CAS. His current research interests include low-dimensional semiconductor materials and devices, memristive materials and devices, and brain-inspired artificial intelligence
      • Corresponding author: lzx_1102@163.com; hmlu@nimte.ac.cn; zhugefei@nimte.ac.cn
      • Received Date: 2024-08-25
      • Revised Date: 2024-10-02
        • Available Online: 2024-11-04

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