Abstract Oxide‐based memristors are considered promising candidates for next‐generation memory and neuromorphic computing applications owing to their intrinsic resistive switching properties, non‐volatile storage, excellent device plasticity, and compatibility with current complementary metal‐oxide‐semiconductor (CMOS) technology. For the physical implementation of neuromorphic microchips, memristors with high power efficiency, reasonable on/off ratios, excellent device reliability, and enhanced plasticity are essential. In this work, a novel memristor design based on SrTiO3‐CeO2(S‐C) vertically aligned nanocomposite (VAN) is proposed, utilizing the highly defective vertical interfaces as migration channels for oxygen vacancies (). More interestingly the interface density (σint) and strain can be effectively tuned by the deposition parameters. Comprehensive analyses, including microstructural analysis, electrical characterization, and finite element modeling, reveal that S‐C VAN memristors with high interface density (HID) exhibit superior power efficiency and reduced device‐to‐device (D2D) variation. Additionally, the S‐C HID memristor demonstrates endurance up to 1012cycles and retains its resistance states for up to 105s at room temperature. The improved plasticity of the S‐C HID memristor enables better linear modulation of conductance with minimal fluctuation over long cycles and across different devices. Furthermore, artificial neural networks (ANNs) incorporating the S‐C HID memristor achieve high accuracy (>95%) on multiple datasets, underscoring its potential for neuromorphic computing applications.
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Enhancing memristor fundamentals through instrumental characterization and understanding reliability issues
Memristors, a two-terminal device, have a resistance that can be changed and retained in two or more different states when subjected to electrical stresses. This unique function makes memristors now an attractive area of research for next-generation electronic devices such as memory and advanced computation. However, credible characterization methods for memristors are not fully established yet to understand fundamental working mechanisms and objectively evaluate figures of merit performance. This review encompasses various characterization methods from materials to electrical characteristics to identify the fundamentals of memristor operations. Meanwhile, large performance variation is the main bottleneck hindering the adoption of this class of devices in practical applications. Thus, the second part of this article focuses on the types of variation and other reliability issues of memristors. Possible strategies to enhance reliability are suggested as well. Topics covered in this review on memristors’ characterization techniques and reliability are of significant relevance to many studies that seek to advance the state of the art in electronic devices and systems towards neuromorphic computing.
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- Award ID(s):
- 2207302
- PAR ID:
- 10451169
- Date Published:
- Journal Name:
- Materials Advances
- Volume:
- 4
- Issue:
- 8
- ISSN:
- 2633-5409
- Page Range / eLocation ID:
- 1850 to 1875
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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