Add BrightSurf on Google Email

Low-power memristor for neuromorphic computing: From materials to applications

06.11.25 | Shanghai Jiao Tong University Journal Center
Anker Laptop Power Bank 25,000mAh (Triple 100W USB-C)

Anker Laptop Power Bank 25,000mAh (Triple 100W USB-C) keeps Macs, tablets, and meters powered during extended observing runs and remote surveys.
Low‑Power Memristor for Neuromorphic Computing: From Materials to Applications
This review describes various types of low-power memristors, demonstrating their potential for a wide range of applications.This review summarizes low-power memristors for multi-level storage, digital logic, and neuromorphic computing, emphasizing their use as artificial synapses and neurons in artificial neural network, convolutional neural network, and spiking neural network, along with 1T1R and 1S1R crossbar array designs.Further exploration is essential to overcome limitations and unlock the Credit: Zhipeng Xia, Xiao Sun, Zhenlong Wang, Jialin Meng , Boyan Jin, Tianyu Wang.

As the demand for artificial intelligence continues to grow, the limitations of traditional von Neumann architecture in terms of energy efficiency and processing speed become more pronounced. Now, researchers from the School of Integrated Circuits at Shandong University, led by Professor Jialin Meng and Professor Tianyu Wang, have presented a comprehensive review on low-power memristors and their potential applications in neuromorphic computing. This work offers valuable insights into the development of next-generation computing technologies that can overcome these limitations.

Why Low-Power Memristors Matter

Innovative Design and Features

Applications and Future Outlook

This comprehensive review provides a roadmap for the development and application of low-power memristors in neuromorphic computing. It highlights the importance of interdisciplinary research in materials science, electronics, and computer science to drive innovation in this field. Stay tuned for more groundbreaking work from Professor Jialin Meng and Professor Tianyu Wang at Shandong University!

Nano-Micro Letters

10.1007/s40820-025-01705-4

Experimental study

Low‑Power Memristor for Neuromorphic Computing: From Materials to Applications

14-Apr-2025

Keywords

Materials

Article Information

Journal
Nano-Micro Letters
DOI
10.1007/s40820-025-01705-4
Method of Research
Experimental study
Article Publication Date
2025-04-14
Article Title
Low‑Power Memristor for Neuromorphic Computing: From Materials to Applications

Contact Information

Bowen Li
Shanghai Jiao Tong University Journal Center
qkzx@sjtu.edu.cn

Source

This article is based on a news release from Shanghai Jiao Tong University Journal Center. BrightSurf curates and republishes science news from research institutions worldwide; the original release is linked below.

Original Source
https://link.springer.com/article/10.1007/s40820-025-01705-4

How to Cite This Article

APA:
Shanghai Jiao Tong University Journal Center. (2025, June 11). Low-power memristor for neuromorphic computing: From materials to applications. Brightsurf News. https://www.brightsurf.com/news/LPEDO9O8/low-power-memristor-for-neuromorphic-computing-from-materials-to-applications.html
MLA:
"Low-power memristor for neuromorphic computing: From materials to applications." Brightsurf News, Jun. 11 2025, https://www.brightsurf.com/news/LPEDO9O8/low-power-memristor-for-neuromorphic-computing-from-materials-to-applications.html.