Research On The Characteristics Of The Circuit Based On Memory Elements And Response To Driving Signals

In 1971, Prof. Leon O. Chua predicted the existence of the memristor and it was realized by HP lab in 2008. As a typical non-linear element, memristor has broad application prospects in the field of chaotic circuits, artificial neural networks and image processing. With the deep research of memristor, researchers promote the concepts of memory components, and then the concepts of memcapacitor and meminductor were proposed. In this paper, we have done research on the basic characteristics of the memristor, memcapacitor and meminductor, and the characteristics of memory elements in the series and parallel circuit, and the application of memory elements in the memory circuit and chaotic circuit. Main contents and results are as following:Firstly, we change the related parameters of the memristor, memcapacitor and meminductor to investigate its basic characteristics. Results show that these three memory elements can show significant tightening hysteresis, and the characteristic curve have significant variation when the device parameters and frequency of the applied signal changes. Secondly, we investigate the MLC series and parallel, RLCM series and RCLM parallel circuit based on these three types of memory devices, and investigate the related characteristics in order to provide a theoretical basis for its application.Lastly, we investigate the response characteristics of memory circuit on external driving signals which contain memristor and memcapacitor. The response characteristics of Chua circuit under the different signals and different excitation signal frequencies and amplitudes were investigated. The results show that the resistance of the memristor, and the capacitance of memcapacitor at periodic excitation signal have significant change, and no significant change in the excitation aperiodic signal. In different excitation signals of different frequencies and amplitudes, the Chua circuit based on the memory element show significant changes in the state. These characteristics have potential application in the field of non-volatile storage, artificial neural networks and etc.