Hyperbolic sine type memristor circuit model

Abstract

The invention discloses a hyperbolic sine type memristor circuit model. An integrated operational amplifier U1 forms a magnetic flux generation circuit, the integrated operational amplifier U1 is usedfor realizing integral operation, additive operation and inverse amplification operation, and an output signal is used as a memristor control signal of a memristor equivalent circuit; the hyperbolicsine type memristor equivalent circuit is composed of an integrated operational amplifier U2 and a multiplier U3. The integrated operational amplifier U2 is used for realizing exponential operation and additive operation, the integrated operational amplifier U2 is connected with the multiplying unit U3 and used for realizing the function of the divider to obtain a required hyperbolic sinusoidal signal, and the multiplying unit U4 is used for multiplying the hyperbolic sinusoidal signal by the voltage quantity to obtain the final memristor current quantity. The invention provides an analog circuit for realizing the characteristics of a memristor, which is used for simulating the magnetic flux charge characteristics of the memristor and replacing an actual memristor for experiment, application and research.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for creating a simulated memory with magnetism that works like other electronic devices or semiconductors. It simplifies the process by combining two different types of components: one produces electrical signals while another generates magnectic charges called currents (magnetic). By doing this simulation we are able to study how these properties affects the behavior of certain materials such as ferrite ceramics used in memristive elements.

Problems solved by technology

The technical problem addressed in this patented paper relates to studying how complicated behavior occurs when activating certain types or structures (neuronal cells) within these systems called memristored synapses. This requires understanding their structure and properties beforehand, especially considering whether they have specific functions such as storing data temporarily without losing them over time.

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