Low-Power Memristor for Neuromorphic Computing: From Materials to Applications
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 advanced multi-value storage, digital logic gates, and analogue neuromorphic computing are summarized. Furthermore, the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed. 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 full potential of low-power memristors for in-memory computing and AI. 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 full potential of low-power memristors for in-memory computing and AI.
Source: Semantic Scholar - Nano-Micro Letters (51 citations) PDF: https://doi.org/10.1007/s40820-025-01705-4 Original Link: https://www.semanticscholar.org/paper/cd520bb278cecd1853649f72609a4aae44790174