Low-Power Memristor for Neuromorphic Computing: From Materials to Applications
Shandong University · Suzhou Research Institute · +2 more institutions
Abstract
As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept and structure of memristor devices are introduced. The selection of functional materials for low-power memristors is discussed, including ion transport materials, phase change materials, magnetoresistive materials, and ferroelectric materials. Two common types of memristor arrays, 1T1R and 1S1R crossbar arrays are introduced, and physical diagrams of edge computing memristor chips are discussed in detail. Potential applications of low-power memristors in…
Citation impact
- FWCI
- 41.29
- Percentile
- 100%
- References
- 147
Authors
6- ZXZhipeng XiaCorresponding
Shandong University, Suzhou Research Institute
- XWXiao Wei Sun
Shandong University, Suzhou Research Institute
- ZWZhenlong Wang
Shandong University, Suzhou Research Institute
- JMJialin Meng
Shanghai International Studies University, Shandong University, Suzhou Research Institute
- BJBoyan Jin
Shandong University, Suzhou Research Institute
Topics & keywords
- Memristor
- Neuromorphic engineering
- Computer science
- Electronic engineering
- Resistive random-access memory
- Computer architecture
- Crossbar switch
- Power (physics)
- Affordable and clean energy