A New Approach to the Fabrication of Memristive Neuromorphic Devices: Compositionally Graded Films.

Jong-Gul Yoon

doi:10.3390/ma13173680

Abstract

Energy-efficient computing paradigms beyond conventional von-Neumann architecture, such as neuromorphic computing, require novel devices that enable information storage at nanoscale in an analogue way and in-memory computing. Memristive devices with long-/short-term synaptic plasticity are expected to provide a more capable neuromorphic system compared to traditional Si-based complementary metal-oxide-semiconductor circuits. Here, compositionally graded oxide films of Al-doped MgxZn1−xO (g-Al:MgZnO) are studied to fabricate a memristive device, in which the composition of the film changes continuously through the film thickness. Compositional grading in the films should give rise to asymmetry of Schottky barrier heights at the film-electrode interfaces. The g-Al:MgZnO films are grown by using aerosol-assisted chemical vapor deposition. The current-voltage (I-V) and capacitance-voltage (C-V) characteristics of the films show self-rectifying memristive behaviors which are dependent on maximum applied voltage and repeated application of electrical pulses. Endurance and retention performance tests of the device show stable bipolar resistance switching (BRS) with a short-term memory effect. The short-term memory effects are ascribed to the thermally activated release of the trapped electrons near/at the g-Al:MgZnO film-electrode interface of the device. The volatile resistive switching can be used as a potential selector device in a crossbar memory array and a short-term synapse in neuromorphic computing.

Highlights

One of the critical issues of current digital computing using conventional von-Neumann architecture is processing bottlenecks that are caused by extensive data transfer between the central processing unit and the memory unit for data-intensive tasks [1]
Neuromorphic computing architectures are the alternatives to the existing computing system [2], which are inspired by parallel information processing in the human brain with high density neural networks and ultra-low power consumption [3]
Significant effort has been devoted to realizing the functionalities of neurons and synapses for neuromorphic computing using emerging nonvolatile memories (NVMs) [9]

Summary

Introduction

One of the critical issues of current digital computing using conventional von-Neumann architecture is processing bottlenecks that are caused by extensive data transfer between the central processing unit and the memory unit for data-intensive tasks [1]. Neuromorphic computing architectures are the alternatives to the existing computing system [2], which are inspired by parallel information processing in the human brain with high density neural networks and ultra-low power consumption [3]. To mimic the human brain, neuromorphic circuits that can process information with massive parallelism and ultra-low power dissipation should be realized. The main technological challenges of neuromorphic computing are the development of memory devices serving the role of synaptic links and/or neuron elements and computing architectures that promise advanced computing functionality with high scalability and low-power operation [4,5,6,7,8]. Significant effort has been devoted to realizing the functionalities of neurons and synapses for neuromorphic computing using emerging nonvolatile memories (NVMs) [9]. Several memristive NVMs, including resistive random-access memory and phase-change memory, have emerged [9,10,11,12,13]

Methods

Results

Conclusion