Floating gate memristor

Abstract

The invention relates to a floating gate memristor. The basic structure of the floating gate memristor successively includes a front electrode, a front dielectric layer, a front floating gate layer, a nanometer battery anode, a nanometer battery electrolyte, a nanometer battery cathode, a rear floating gate layer, a rear dielectric layer and a rear electrode, wherein the front electrode, the front dielectric layer, the front floating gate layer and the nanometer battery anode simulate a presynaptic membrane which converting electronic signals to ion signals by utilizing electronic tunnel transfer and field effect; the nanometer battery electrolyte serves an ion channel simulating a synaptic cleft; the nanometer battery cathode, the rear floating gate, the rear dielectric layer and the rear electrode simulate a subsynaptic membrane which converts the ion signals to electronic signals. The floating gate memristor is stable in reading and writing, good in controllability, simple in structure, compatible to CMOS, easy to integrate, suitable for large scale production and commercialization and capable of promoting development of nervous system calculation and encephaloid calculation.

Description

Technical field [0001] The invention relates to a floating gate memristor, which relates to the field of neuromorphic computing based on the memristor, in particular to an ionic synaptic memristor based on a nano battery and a floating gate of a transistor. Background technique [0002] Neural network-based intelligent learning systems have made remarkable achievements, such as Google's DeepMind's Go artificial intelligence AlphaGo, Carnegie Mellon University's Texas Hold'em program, and Stanford University's skin cancer recognition program. Artificial intelligence systems based on neural networks have been involved in all aspects of finance, medical care, transportation and the environment. The basic building blocks of neural networks are synapses and neurons. For a long time, people have been trying to develop electronic synapses. In 1971, Professor Cai Shaotang from the Department of Electronic Engineering of the University of California, Berkeley first proposed the memristor ...

AI Technical Summary

Problems solved by technology

However, it is very difficult to use a two-terminal device to read the change in conductance caused by the change of ion distribution, because electrons basically cannot pass through the dielectric of the nanobattery, and the migration history of ions is only expressed as the ion concentration in the positive and negative materials. It is necessary to introduce a new structure to record and read the ion migration history in the nanobattery to simulate the synaptic behavior

Images