Self-driven triboelectric nanogenerating synaptic transistor

Abstract

The self-driven triboelectric nanogenerator synapse transistor involved in the present invention includes a triboelectric nanogenerator and a synapse transistor, which includes a substrate; an electrode layer formed on the substrate; a shared intermediate layer formed on the electrode layer; a synaptic transistor active layer, a source electrode, and a drain electrode on the intermediate layer; and a positive friction layer and a negative friction layer formed on a shared intermediate layer serving as a dielectric layer of the synaptic transistor and a triboelectric nanogenerator The middle layer improves the output voltage of the triboelectric nanogenerator and can realize the synaptic function of the synaptic transistor. The electrode layer is used as the output electrode of the triboelectric nanogenerator and the gate electrode of the synaptic transistor. The structure is simple, light and flexible, and the friction The positive friction layer or the negative friction layer, sharing the middle layer to generate pulse voltage, can generate excitatory post-synaptic current between the source electrode and the drain electrode without external power supply, and can self-drive to realize the function of bionic synaptic transistor.

Description

Technical field [0001] The present invention relates to a self-driven friction nano-generating synaptic transistor belonging to the technical field of semiconductor devices. Background technique [0002] The demand for big data and high-efficiency human machines brought by intelligent era, bring higher requirements for complex information processing and storage. Compared to the low power and small volume of the human brain, the current computer system has a significant shortcomings. In recent years, the information processing method of imitating the human brain has been developed, and a class of neural network computing systems has provided a potential approach to high efficiency operations. At present, most of the neural network systems use traditional complementary metal oxide semiconductor technologies, although similarly to the computational function of neural networks can be implemented under the condition of the combined code algorithm, its efficiency is still much lower th...

AI Technical Summary

Problems solved by technology

However, in recent years, research has mainly realized the integration of bionic synaptic transistors and stimulus acquisition terminals through additional complex circuits and requires additional power supply, which is not conducive to the application of flexible electronic devices, wearable devices and bionic skin.

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