Artificial neuron structure and its preparation method, signal and time extraction method

Abstract

The present invention provides an artificial neuron structure, a preparation method thereof, and a signal and time extraction method, wherein the artificial neuron structure includes: a substrate; a back gate metal layer located under the substrate; an epitaxial layer located under the substrate Above the substrate; two non-contact source and drain metal layers, located above the epitaxial layer; and an organic thin film layer, respectively in contact with the two source and drain metal layers, and stacked on the epitaxial layer wherein, the organic thin film layer is provided with two openings for exposing at least part of the two source and drain metal layers. By simulating the working principle of biological neurons, the artificial neuron structure uses the charge provided by the electrochemical reaction to regulate the carrier concentration distribution in the semiconductor channel, and realizes the corresponding output for various electrical stimuli; it has the characteristics of time-varying; it can The power-off operation reduces the static power consumption to zero, and is suitable for low-power circuit applications; it also has a very wide application prospect.

Description

technical field [0001] The invention relates to the field of semiconductor device technology and artificial intelligence, in particular to an artificial neuron structure, a preparation method thereof, and a signal and time extraction method. Background technique [0002] The artificial neural network based on the combination of neural network algorithm and high-performance computing is a hot spot in the field of artificial intelligence, and it is also a technological highland that major technology companies are vying for. In the above-mentioned artificial neural network, high-performance computing is usually accomplished mainly by using a commercial high-performance central processing unit (CPU) and a graphics processing unit (GPU) combined with advanced neural network algorithms. Compared with the real biological neural network, the above-mentioned artificial neural network has obvious advantages in speed, but there is still a big gap in terms of power consumption and integ...

AI Technical Summary

Problems solved by technology

The above devices or circuits have a common problem: the static power consumption is relatively large

However, the introduction of the above-mentioned new materials and new structures has limited reduction in device power consumption.

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