Key Takeaways & Executive Findings
- •• 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. • The paper discusses the selection of functional materials for low-power memristors, including ion transport, phase change, magnetoresistive, and ferroelectric materials.
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.
1. Introduction
Von Neumann architecture is the basic architecture of modern computers, proposed by mathematician John von Neumann in 1945. Its core idea is to store program instructions and data in the same memory block and process the data by reading and executing these instructions through a central processing unit (CPU). This architecture’s primary benefit lies in its adaptability and malleability, allowing the computer to undertake various tasks by altering programs stored in its memory [1]. However, von Neumann structure has its inherent flaws, where data storage and computing share the same channel. Such working mode limits processing speed of computer, especially if it uses dynamic random access memory (DRAM) as its primary memory. DRAM access not only requires high energy consumption, but also requires periodic refreshing. During data processing, the processor has to run continuously even while waiting for data, leading to additional energy consumption. As a result, the so-called “energy wall” and “speed wall” are formed.
As internet technology rapidly evolves, the demand for artificial intelligence is experiencing exponential growth. Artificial intelligence has achieved numerous breakthroughs in various domains, including image processing, natural language processing, and big data analysis [2–4]. The amount of data that need to be trained and processed are also increasing daily. To address this problem, complex hardware systems consisting of numerous CPUs and graphics processing units (GPUs) have been developed. As semiconductor technology is approaching its physical limits, Moore’s law is also facing failure [5, 6], and researchers must examine the constraints of von Neumann architecture through the lens of computer architecture and software algorithms. In this regard, researchers have proposed various approaches, such as the introduction of multi-level caches [7], the introduction of data streaming [8], and the proposal of in-memory computing. Among emerging technologies, in-memory computing, first conceptualized by W.H. Kautz in 1969 [9], seamlessly integrates computational functions within storage, drastically reducing the delay for data transfer. This integration further leads to reduced power consumption and improved efficiency and is hailed as the next-generation computer architecture poised to transcend the barriers of von Neumann architecture. In recent years, there has been a swift advancement in the development of novel non-volatile memory and in-memory computing technology. With high speed, low power consumption and high-density integration capability, memristor is becoming a research hotspot in in-memory computing fields. Inspired by human brain, memristors with weights updating functions are considered ideal for developing in-memory computing and artificial intelligence [10].
Loading authentic research manuscript (Pages 1–5)...
Zhipeng Xia, Xiao Sun, Zhenlong Wang, Jialin Meng, Boyan Jin, Tianyu Wang (2025). Low-Power Memristor for Neuromorphic Computing: From Materials to Applications. Nano-Micro Letters. https://doi.org/10.1007/s40820-025-01705-4
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is a memristor?
A memristor is an emerging memory device that exhibits a relationship between electric charge and magnetic flux, and its resistance can be programmed by applying voltage or current. It is considered a promising candidate for neuromorphic computing due to its low power consumption and ability to emulate synaptic behavior.
Why are low-power memristors important for neuromorphic computing?
Low-power memristors are crucial for neuromorphic computing because they can mimic biological synapses and neurons with minimal energy consumption, enabling energy-efficient artificial neural networks and edge computing applications.
What are the main types of materials used in low-power memristors?
The main types of functional materials for low-power memristors include ion transport materials, phase change materials, magnetoresistive materials, and ferroelectric materials, each offering distinct mechanisms for resistance switching.
What are 1T1R and 1S1R crossbar arrays?
1T1R and 1S1R crossbar arrays are common architectures for memristor-based memory and computing systems. In 1T1R, each memristor is paired with a transistor to control access, while in 1S1R, a selector device is used to mitigate sneak paths, enabling high-density integration and reliable operation.
What are the future challenges for memristor-based neuromorphic computing?
Future challenges include improving device variability, endurance, and scalability, as well as developing efficient algorithms and architectures that fully exploit the capabilities of memristors for in-memory computing and artificial intelligence.
Related Technical Papers & Translations
Direct Repair of the Crystal Structure and Coating Surface of Spent LiFePO4 Materials Enables Superfast Li-Ion Migration
The rapid accumulation of spent LiFePO4 (LFP) cathodes from retired lithium-ion batteries necessitates the development of effective and environmental-friendly recycling strategies. In this context, direct regeneration has emerged as a promising approach for reclaiming LFP cathode materials, offering a streamlined pathway to restore their electrochemical functionality. We report an integrated regeneration protocol that simultaneously repairs the degraded crystal structure and reconstructs the damaged carbon coating in spent LFP. The regenerated cathode material had superfast lithium-ion diffusion kinetics and a stable cathode–electrolyte interface, giving a remarkable rate capability with specific capacities of 122 mAh g−1 at 5C and 106 mAh g−1 at 10C (1C = 170 mA g−1). It also maintained capacities of 110.7 mAh g−1 (5C) and 84.1 mAh g−1 (10C) after 400 cycles. It could be used in harsh environments and could be stably cycled at subzero temperatures (−10 and −20 °C) and in solid-state electrolyte batteries. Life cycle assessment combined with economic evaluation using the EverBatt model reveals that this direct regeneration approach has high economic and environmental benefits.
Oxide Semiconductor for Advanced Memory Architectures: Atomic Layer Deposition, Key Requirement and Challenges
Oxide semiconductors (OSs), introduced by the Hosono group in the early 2000s, have evolved from display backplane materials to promising candidates for advanced memory and logic devices. The exceptionally low leakage current of OSs and compatibility with three-dimensional (3D) architectures have recently sparked renewed interest in their use in semiconductor applications. This review begins by exploring the unique material properties of OSs, which fundamentally originate from their distinct electronic band structure. Subsequently, we focus on atomic layer deposition (ALD), a core technique for growing excellent OS films, covering both basic and advanced processes compatible with 3D scaling. The basic surface reaction mechanisms—adsorption and reaction—and their roles in film growth are introduced. Furthermore, material design strategies, such as cation selection, crystallinity control, anion doping, and heterostructure engineering, are discussed. We also highlight challenges in memory applications, including contact resistance, hydrogen instability, and lack of p-type materials, and discuss the feasibility of ALD-grown OSs as potential solutions. Lastly, we provide an outlook on the role of ALD-grown OSs in memory technologies. This review bridges material fundamentals and device-level requirements, offering a comprehensive perspective on the potential of ALD-driven OSs for next-generation semiconductor memory devices.
Laser powder bed fusion of biodegradable Zn-4Cu alloy: Processing, microstructure and properties
Zn's natural degradability and biocompatibility make it a promising candidate for implants, however, its mechanical properties remain insufficient for bone applications. In this study, the performance of Zn was enhanced by developing Zn-Cu alloys via laser powder bed fusion (LPBF). Optimal LPBF parameters for forming stable tracks were achieved by adjusting laser power and scanning speed. Under optimized conditions of 100 W and 100 mm/s, high-density (99.58%) Zn-Cu alloys with improved hardness (68.2HV) and yield strength (160 MPa) were achieved. These improvements are attributed to solid solution strengthening, segregation strengthening, and grain refinement. The Zn-Cu alloys also demonstrated favorable degradation behavior, with a rate of 0.16 mm/year. This degradation is primarily driven by micro-galvanic corrosion between the CuZn5 phase and Zn matrix, along with refined grains and increased grain boundary density. This work demonstrates a viable strategy for fabricating Zn-based implants with enhanced structural integrity and mechanical performance via LPBF.