Carbon-based multi-terminal bionic synaptic device and preparation method thereof

Abstract

The invention relates to a carbon-based multi-terminal bionic synapse device and a preparation method thereof, the bionic synapse device comprises a substrate, a conductive layer and a dielectric layer from bottom to top, the upper surface of the dielectric layer is respectively provided with a first gate electrode, a second gate electrode, a source electrode and a drain electrode, and the source electrode and the drain electrode are connected through a semiconductor channel. The conductive layer comprises a metal conductive layer and a semiconductor conductive layer which are arranged from bottom to top. The preparation method comprises the following steps: depositing a metal conductive layer on one surface of a substrate; forming a semiconductor conductive layer on the obtained metal conductive layer; forming a dielectric layer on the obtained substrate; developing a gate electrode pattern, a source electrode pattern and a drain electrode pattern on the obtained dielectric layer, and depositing an electrode metal material on the gate electrode pattern, the source electrode pattern and the drain electrode pattern to form a gate electrode, a source electrode and a drain electrode; and forming a semiconductor channel between the obtained source electrode and drain electrode to obtain the carbon-based multi-terminal bionic synaptic device. The neural synapse learning and memorizing device can realize neural synapse learning and memorizing functions.

Description

technical field [0001] The invention relates to the technical field of bionic synaptic devices, in particular to a carbon-based multi-terminal bionic synaptic device and a preparation method thereof. Background technique [0002] Synapses provide functional connections between neurons and store information by modulating the strength of the connections. The biological nervous system composed of neurons and synapses has the characteristics of parallelism, low power consumption, fault tolerance, self-learning and robustness. Artificial synaptic electronics and neuromorphic systems have the potential to overcome the traditional von Neumann bottleneck and create a new paradigm for complex problems such as pattern recognition, image classification, decision making, and associative learning. [0003] Traditional artificial synapses are simulated using a large number of complementary metal-oxide-semiconductors (CMOS), which consume high power and require complex integrated circuits...

AI Technical Summary

Problems solved by technology

However, when these two-terminal devices are integrated in the system, additional circuit components are usually required to select the target device

Furthermore, in double-terminal synaptic devices, it is difficult to simultaneously carry out signal transmission and self-learning

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