Abstract

We experimentally investigated the effect of post-growth annealing on the morphological, structural, and electrophysical parameters of nanocrystalline ZnO films fabricated by pulsed laser deposition. The influence of post-growth annealing modes on the electroforming voltage and the resistive switching effect in ZnO nanocrystalline films is investigated. We demonstrated that nanocrystalline zinc oxide films, fabricated at certain regimes, show the electroforming-free resistive switching. It was shown, that the forming-free nanocrystalline ZnO film demonstrated a resistive switching effect and switched at a voltage 1.9 ± 0.2 V from 62.42 ± 6.47 (RHRS) to 0.83 ± 0.06 kΩ (RLRS). The influence of ZnO surface morphology on the resistive switching effect is experimentally investigated. It was shown, that the ZnO nanocrystalline film exhibits a stable resistive switching effect, which is weakly dependent on its nanoscale structure. The influence of technological parameters on the resistive switching effect in a forming-free ZnO nanocrystalline film is investigated. The results can be used for fabrication of new-generation micro- and nanoelectronics elements, including random resistive memory (ReRAM) elements for neuromorphic structures based on forming-free ZnO nanocrystalline films.

Highlights

  • In recent years, the relevance of high-performance computing related to unstructured data classification and pattern recognition has been growing [1,2,3,4,5]

  • The technical implementation of parallelism will allow us to efficiently solve problems associated with multi-parameter nonlinear optimization [9,10], discriminant analysis [11], and clustering methods [12,13] to create systems related to pattern recognition, construction of predictive control systems, and cognitive information processing devices [14]

  • The purpose of this work is to study the effect of post-growth annealing modes on the morphological, structural, and electrophysical parameters of ZnO nanocrystalline films obtained by the pulsed laser deposition (PLD), as well as to study the effect of post-growth annealing modes on the forming-free and the resistive switching effect in ZnO nanocrystalline films

Read more

Summary

Introduction

The relevance of high-performance computing related to unstructured data classification and pattern recognition has been growing [1,2,3,4,5]. The development of integrated electronics technology leads to an increase in processor performance and an increase in memory volume, but the rate of data exchange between them remains almost unchanged This issue is common to all computing systems built based on von Neumann architecture, the principle of which is the physical separation of the processor and memory. The information processing steps are performed sequentially and are limited by the data bus bandwidth (Von Neumann bottleneck) [6,7,8] It follows that computers based on von Neumann architecture do not efficiently solve problems related to real-time image recognition, diagnostics of various processes, as well as in applications related to self-learning and adaptive control systems. The technical implementation of parallelism will allow us to efficiently solve problems associated with multi-parameter nonlinear optimization [9,10], discriminant analysis [11], and clustering methods [12,13] to create systems related to pattern recognition, construction of predictive control systems, and cognitive information processing devices [14]

Objectives
Methods
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call