{"id":212416,"date":"2021-03-26T19:22:24","date_gmt":"2021-03-26T16:22:24","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/a-bio-inspired-mechano-photonic-artificial-synapse\/"},"modified":"2021-03-26T19:22:24","modified_gmt":"2021-03-26T16:22:24","slug":"a-bio-inspired-mechano-photonic-artificial-synapse","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/a-bio-inspired-mechano-photonic-artificial-synapse\/","title":{"rendered":"#A bio-inspired mechano-photonic artificial synapse"},"content":{"rendered":"

“#A bio-inspired mechano-photonic artificial synapse<\/strong>”<\/p>\n

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\n Biological tactile\/visual neurons and mechano-photonic artificial synapse. (A) Schematic illustrations of biological tactile\/visual sensory system. (B) Schematic diagram of the mechano-photonic artificial synapse based on graphene\/MoS2 (Gr\/MoS2) heterostructure. (i) Top-view scanning electron microscope (SEM) image of the optoelectronic transistor; scale bar, 5 \u03bcm. The cyan area indicates the MoS2 flake, while the white strip<\/a> is graphene. (ii) Illustration of charge transfer\/exchange for Gr\/MoS2 heterostructure. (iii) Output mechano-photonic signals from the artificial synapse for image recognition. Credit: Science<\/a> Advances, doi: 10.1126\/sciadv.abd9117
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Multifunctional and diverse artificial neural systems can incorporate multimodal plasticity, memory and supervised learning functions to assist neuromorphic computation<\/a>. In a new report, Jinran Yu and a research team in nanoenergy, nanoscience and materials science in China and the US., presented a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The team used an optoelectronic transistor made of graphene\/molybdenum disulphide (MoS2<\/sub>) heterostructure and an integrated triboelectric nanogenerator to compose the artificial synapse. They controlled the charge transfer\/exchange in the heterostructure with triboelectric potential and modulated the optoelectronic synapse behaviors readily, including postsynaptic photocurrents, photosensitivity and photoconductivity. The mechano-photonic artificial synapse is a promising implementation to mimic the complex biological nervous system and promote the development of interactive artificial intelligence. The work is now published on Science Advances<\/i>.<\/p>\n


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Brain-inspired neural networks.<\/b><\/p>\n

The human brain can integrate cognition, learning and memory tasks via auditory, visual, olfactory and somatosensory interactions<\/a>. This process is difficult to be mimicked using conventional von Neumann architectures that require additional sophisticated functions<\/a>. Brain-inspired neural networks are made of various synaptic devices to transmit information and process using the synaptic weight. Emerging photonic synapse combine the optical and electric neuromorphic modulation and computation to offer a favorable option with high bandwidth, fast speed and low cross-talk to significantly reduce power consumption<\/a>. Biomechanical motions including touch, eye blinking and arm waving are other ubiquitous triggers or interactive signals to operate electronics during artificial synapse plasticization<\/a>. In this work, Yu et al. presented a mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The device contained an optoelectronic transistor and an integrated triboelectric nanogenerator<\/a> (TENG) in contact-separation mode. The mechano-optical artificial synapses have huge functional potential as interactive optoelectronic interfaces, synthetic retinas and intelligent robots.<\/p>\n

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\n Synergistic effect of mechanical and visual signal for mechano-photonic artificial synapse. (A) Schematic diagram of the synergistic mechanical and optical signal for mechano-photonic artificial synapse. (B) \u2212\u0394PSCs under different PLED\u2019s at a fixed D of 1 mm, VD = 1 V, and light pulse width of 0.5 s. Inset: The peak current of \u0394PSC versus PLED. (C) \u2212\u0394PSCs under different PLED\u2019s when the light is turned off. (D) \u2212\u0394PSCs at different light pulse width (PLED = 3.5 mW cm\u22122 and D = 1 mm). (E) The \u2212\u0394PSCs under 40 consecutive light pulses under different displacements (PLED = 3.5 mW cm\u22122; pulse width, 50 ms; D = 0.5, 1, and 1.5 mm). (F) The conductance margins (Gmax\/Gmin) as a function of displacement. Credit: Science Advances, doi: 10.1126\/sciadv.abd9117
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The mechano-photonic artificial synapse<\/b>
\nThe human brain and its associated biomechanical and visual sensations are critical to acquire somatosensory and visual information. The brain contains a variety of neurons that receive interactive signals through varied modes to implement neuromorphic computation in the multisensory association area. Synapses from important points of connection<\/a> between two adjacent neurons during neural information transmissions. Yu et al. were bioinspired by the brain and nervous system to develop a mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The mechano-photonic artificial synapse included an optoelectronic transistor and integrated TENG (triboelectric nanogenerator). During the experiments, the team used chemical vapor deposition<\/a> to deposit monolayer graphene on the optoelectronic transistor, which they then stacked on a multilayer molybdenum sulfide (MoS2<\/sub>) flake on a silicon dioxide substrate. Using the experimental setup, Yu et al. could realize the synergistic optical and mechanical modulation on the synaptic plasticity. <\/p>\n

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\n Mechano-optoelectronic transistor based on Gr\/MoS2 heterostructure and corresponding working mechanism. (A) The TENG output voltage (VTENG) versus displacement (D). Inset: Equivalent circuit diagram for VTENG characterization. (B) Transfer curves (ID versus D) in the dark and under different green light power intensity (PLED). (C) Working mechanism of the mechano-optoelectronic transistor based on Gr\/MoS2 heterostructure. Schematic illustrations of the working principles and the corresponding energy band diagram at (i) initial flat-band state, (ii) separation state (D+), and (iii) contact state (D\u2212). Credit: Science Advances, doi: 10.1126\/sciadv.abd9117
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Testing the device and mechano-optical characterization.<\/b><\/p>\n

To test the practicality of triboelectric potential gating, the team characterized the TENG output voltage versus mechanical displacement using a test circuit, where they connected the transistor dielectric capacitor and test system capacitor with TENG in parallel. By optimizing the MoS2 <\/sub>thickness in the heterostructure, Yu et al. improved the device photosensitivity and electrical performance to potentially influence the device for system-level app<\/a>lications. To characterize the mechano-optoelectronic transistor, they measured the output performance under synergistic effects for TENG displacement and illumination with green LED at different power intensities. To then understand the tunable photoresponsivity of the mechano-photonic transistor of the device, they studied the influence of mechanical displacement on the photocurrent and photosensitivity. A more positive displacement can induce larger photocurrent and higher photo-receptivity relative to the electric field-dependent Fermi-level<\/a> and electronic states in the heterostructure. The mechanical displacement related photoresponse improved the photosensitivity of the device while regulating the feature at a desired level on demand.<\/p>\n

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