Abstract
As the fourth basic electronic component, the application fields of the memristive devices are diverse. The digital resistive switching with sudden resistance change is suitable for the applications of information storage, while the analog memristive devices with gradual resistance change are required in the neural system simulation. In this paper, a transparent device of ZnO films deposited by the magnetron sputtering on indium tin oxides (ITO) glass was firstly prepared and found to show typical analog memristive switching behaviors, including an I–V curve that exhibits a ‘pinched hysteresis loops’ fingerprint. The conductive mechanism of the device was discussed, and the LTspice model was built to emulate the pinched hysteresis loops of the I–V curve. Based on the LTspice model and the Pavlov training circuit, a conditioned reflex experiment has been successfully completed both in the computer simulation and the physical analog circuits. The prepared device also displayed synapses-like characteristics, in which resistance decreased and gradually stabilized with time under the excitation of a series of voltage pulse signals.
Highlights
Since the laboratory of Hewlett-Packard (HP) Development Company (Palo Alto, CA, USA)announced that the ‘missing’ circuit elements were found in 2008, memristive devices have attracted a great deal of attention worldwide as a potential element for future device applications, such as nonvolatile information storage and artificial neural network
zinc oxide (ZnO)/indium tin oxides (ITO), and positive bias was defined as the current flow from
ZnO/ITO, and positive bias was defined as the current flow from ZnO to ITO
Summary
Announced that the ‘missing’ circuit elements were found in 2008, memristive devices have attracted a great deal of attention worldwide as a potential element for future device applications, such as nonvolatile information storage and artificial neural network. The artificial neural network invariably needs analog memristive devices to implement synapses [1]. Smooth current–voltage curves with gradual change of the device resistance are the fingerprints of analog memristive devices, which are always known as analog resistive switching or analog memristor. There are considerable studies of analog memristor-based neural networks, because the gradual resistance provides a way to mimic synaptic strength and synaptic plasticity [2], and the advantages of a simple structure can be compared with pure software implementations [3,4].
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