The human somatosensory system, consisting of receptors, transmitters, and synapses, functions as the medium for external mechanical stimuli perception and sensing signal delivery/processing. Developing sophisticated artificial sensory synapses with a high performance, uncomplicated fabrication process, and low power consumption is still a great challenge. Here, a piezotronic graphene artificial sensory synapse developed by integrating piezoelectric nanogenerator (PENG) with an ion gel-gated transistor is demonstrated. The piezopotential originating from PENG can efficiently power the synaptic device due to the formation of electrical double layers at the interface of the ion gel/ electrode and ion gel/graphene. Meanwhile, the piezopotential coupling is capable of linking the spatiotemporal strain information (strain amplitude and duration) with the postsynaptic current. The synaptic weights can be readily modulated by the strain pulses. Typical properties of a synapse including excitation/inhibition, synaptic plasticity, and paired pulse facilitation are successfully demonstrated. The dynamic modulation of a sensory synapse is also achieved based on dual perceptual presynaptic PENGs coupling to a single postsynaptic transistor. This work provides a new insight into developing piezotronic synaptic devices in neuromorphic computing, which is of great significance in future self-powered electronic skin with artificial intelligence, a neuromorphic interface for neurorobotics, human-robot interaction, an intelligent piezotronic transistor, etc.
Developing multifunctional and diversified artificial neural systems to integrate multimodal plasticity, memory, and supervised learning functions is an important task toward the emulation of neuromorphic computation. Here, we present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The artificial synapse is composed of an optoelectronic transistor based on graphene/MoS2 heterostructure and an integrated triboelectric nanogenerator. By controlling the charge transfer/exchange in the heterostructure with triboelectric potential, the optoelectronic synaptic behaviors can be readily modulated, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity. The photonic synaptic plasticity is elaborately investigated under the synergistic effect of mechanical displacement and the light pulses embodying different spatiotemporal information. Furthermore, artificial neural networks are simulated to demonstrate the improved image recognition accuracy up to 92% assisted with mechanical plasticization. The mechano-photonic artificial synapse is highly promising for implementing mixed-modal interaction, emulating complex biological nervous system, and promoting the development of interactive artificial intelligence.
Low power electronics endowed with artificial intelligence and biological afferent characters are beneficial to neuromorphic sensory network. Highly distributed synaptic sensory neurons are more readily driven by portable, distributed, and ubiquitous power sources. Here, we report a contact-electrification-activated artificial afferent at femtojoule energy. Upon the contact-electrification effect, the induced triboelectric signals activate the ion-gel-gated MoS2 postsynaptic transistor, endowing the artificial afferent with the adaptive capacity to carry out spatiotemporal recognition/sensation on external stimuli (e.g., displacements, pressures and touch patterns). The decay time of the synaptic device is in the range of sensory memory stage. The energy dissipation of the artificial afferents is significantly reduced to 11.9 fJ per spike. Furthermore, the artificial afferents are demonstrated to be capable of recognizing the spatiotemporal information of touch patterns. This work is of great significance for the construction of next-generation neuromorphic sensory network, self-powered biomimetic electronics and intelligent interactive equipment.
The emulation of synaptic plasticity to achieve sophisticated cognitive functions and adaptive behaviors is critical to the evolution of neuromorphic computation and artificial intelligence. More feasible plastic strategies (e.g., mechanoplasticity) are urgent to achieve comparable, versatile, and active cognitive complexity in neuromorphic systems. Here, a versatile mechanoplastic artificial synapse based on tribotronic floating-gate MoS 2 synaptic transistors is proposed. Mechanical displacement can induce triboelectric potential coupling to the floating-gate synaptic transistor, trigger a postsynaptic current signal, and modulate the synaptic weights, which realizes the synaptic mechanoplasticity in an active and interactive way. Typical synaptic plasticity behaviors including potentiation/inhibition and paired pulse facilitation/depression are successfully imitated. Assistant with the charge trapping by floating gate, the artificial synapse can realize mechanical displacement derived short-term and long-term plasticity simultaneously. A facile artificial neural network is also constructed to demonstrate an adding synaptic weight and neuromorphic logic switching (AND, OR) by mechanoplasticity without building complex complementary metal oxide semiconductor circuits. The proposed mechanoplastic artificial synapse offers a favorable candidate for the construction of mechanical behavior derived neuromorphic devices to overcome the von Neumann bottleneck and perform advanced synaptic behaviors.
Iontronics are effective in modulating electrical properties through the electric double layers (EDLs) assisted with ionic migration/ arrangement, which are highly promising for unconventional electronics, ionic sensory devices, and flexible interactive interface. Proton conductors with the smallest and most abundant protons (H + ) can realize a faster migration/ polarization under electric field to form the EDL with higher capacitance. Here, a versatile triboiontronic MoS 2 transistor via proton conductor by sophisticated combination of triboelectric modulation and protons migration has been demonstrated. This device utilizes triboelectric potential originated from mechanical displacement to modulate the electrical properties of transistors via protons migration/accumulation. It shows superior electrical properties, including high current on/off ratio over 10 6 , low cutoff current (∼0.04 pA), and steep switching properties (89 μm/dec). Pioneering noise tests are conducted to the tribotronic devices to exclude the possible noise interference introduced by mechanical displacement. The versatile triboiontronic MoS 2 transistor via proton conductor has been utilized for mechanical behavior derived logic devices and an artificial sensory neuron system. This work represents the reliable and effective triboelectric potential modulation on electronic transportation through protonic dielectrics, which is highly desired for theoretical study of tribotronic gating, active mechanosensation, self-powered electronic skin, artificial intelligence, etc.
In article number 1900959, Qijun Sun and co‐workers develop a piezotronic graphene artificial synapse based on a piezopotential powered/modulated electrical double layer graphene field effect transistor to correlate the spatiotemporal information of the external strains with the excitatory postsynaptic current. Typical properties of a neuron synapse, such as potentiation/inhibition, plasticity, paired‐pulse facilitation, and dynamic logic functions are demonstrated.
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