Sub-nanosecond memristor based on ferroelectric tunnel junction.

Nat Commun

Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China.

Published: March 2020

Next-generation non-volatile memories with ultrafast speed, low power consumption, and high density are highly desired in the era of big data. Here, we report a high performance memristor based on a Ag/BaTiO/Nb:SrTiO ferroelectric tunnel junction (FTJ) with the fastest operation speed (600 ps) and the highest number of states (32 states or 5 bits) per cell among the reported FTJs. The sub-nanosecond resistive switching maintains up to 358 K, and the write current density is as low as 4 × 10 A cm. The functionality of spike-timing-dependent plasticity served as a solid synaptic device is also obtained with ultrafast operation. Furthermore, it is demonstrated that a Nb:SrTiO electrode with a higher carrier concentration and a metal electrode with lower work function tend to improve the operation speed. These results may throw light on the way for overcoming the storage performance gap between different levels of the memory hierarchy and developing ultrafast neuromorphic computing systems.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080735PMC
http://dx.doi.org/10.1038/s41467-020-15249-1DOI Listing

Publication Analysis

Top Keywords

memristor based
8
ferroelectric tunnel
8
tunnel junction
8
operation speed
8
sub-nanosecond memristor
4
based ferroelectric
4
junction next-generation
4
next-generation non-volatile
4
non-volatile memories
4
memories ultrafast
4

Similar Publications

IMPACT: In-Memory ComPuting Architecture based on Y-FlAsh Technology for Coalesced Tsetlin machine inference.

Philos Trans A Math Phys Eng Sci

January 2025

Microsystems Group, School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.

The increasing demand for processing large volumes of data for machine learning (ML) models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-architectural level, significantly reducing both latency and energy. In this article, we present In-Memory comPuting architecture based on Y-FlAsh technology for Coalesced Tsetlin machine inference (IMPACT), underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm complementary metal oxide semiconductor (CMOS) process.

View Article and Find Full Text PDF

Memristive technology mitigates the memory wall issue in von Neumann architectures by enabling in-memory data processing. Unlike traditional complementary metal-oxide semiconductor (CMOS) technology, memristors provide a new paradigm for implementing cryptographic functions and security considerations. While prior research explores memristors for cryptographic functions and side-channel attack vulnerabilities, our study uniquely addresses memristor-oriented countermeasures.

View Article and Find Full Text PDF

Neural reuse can drive organisms to generalize knowledge across various tasks during learning. However, existing devices mostly focus on architectures rather than network functions, lacking the mimic capabilities of neural reuse. Here, we demonstrate a rational device designed based on ferroionic CuInPS, to accomplish the neural reuse function, enabled by dynamic allocation of the ferro-ionic phase.

View Article and Find Full Text PDF

Implementation of memristive emotion associative learning circuit.

Cogn Neurodyn

December 2025

School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018 China.

Psychological studies have demonstrated that the music can affect memory by triggering different emotions. Building on the relationships among music, emotion, and memory, a memristor-based emotion associative learning circuit is designed by utilizing the nonlinear and non-volatile characteristics of memristors, which includes a music judgment module, three emotion generation modules, three emotional homeostasis modules, and a memory module to implement functions such as learning, second learning, forgetting, emotion generation, and emotional homeostasis. The experimental results indicate that the proposed circuit can simulate the learning and forgetting processes of human under different music circumstances, demonstrate the feasibility of memristors in biomimetic circuits, verify the impact of music on memory, and provide a foundation for in-depth research and application development of the interaction mechanism between emotion and memory.

View Article and Find Full Text PDF

All-Optically Controlled Memristive Device Based on CuO/TiO Heterostructure Toward Neuromorphic Visual System.

Research (Wash D C)

January 2025

Key Laboratory for UV Light-Emitting Materials and Technology (Ministry of Education), College of Physics, Northeast Normal University, Changchun, China.

The optoelectronic memristor integrates the multifunctionalities of image sensing, storage, and processing, which has been considered as the leading candidate to construct novel neuromorphic visual system. In particular, memristive materials with all-optical modulation and complementary metal oxide semiconductor (CMOS) compatibility are highly desired for energy-efficient image perception. As a p-type oxide material, CuO exhibits outstanding theoretical photoelectric conversion efficiency and broadband photoresponse.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!