Electrospinning Equipment for Research| Low-Power Memristor for NeuromorphicComputing: From Materials to Applications

Prof. Wang Tianyu (Shandong University) Review in Nano-Micro Letters: Low-Power Neuromorphic Computing Devices, from Materials to Applications

As an emerging memory device, memristors have demonstrated great potential in neuromorphic computing due to their low-power characteristics. 

Recently, Prof. Wang Tianyu and Prof. Meng Jialin from Shandong University published a review summarizing the materials, device structures, and applications of low-power memristors, with a focus on their implementations in artificial neural networks (ANNs), convolutional neural networks (CNNs), and spiking neural networks (SNNs). The potential of memristors as artificial synapses and neurons is thoroughly demonstrated, while their applications in multi-level storage, digital logic gates, and in-memory computing are also discussed. Finally, current challenges and future research directions are presented.

The review article titled "Low-Power Memristor for Neuromorphic Computing: From Materials to Applications" was published in the renowned journal Nano-Micro Letters. Prof. Wang Tianyu and Prof. Meng Jialin from the Institute of Integrated Circuits at Shandong University are the corresponding authors, with Xia Zhipeng as the first author.

This review explores the applications of low-power memristors in neuromorphic computing systems, focusing on their materials, device architectures, and critical roles in neural networks.

Part 1: Various functional materials for memristors are introduced, including ion-transport materials, phase-change materials, magnetoresistive materials, and ferroelectric materials, along with their corresponding conduction mechanisms.

Part 2: Two primary memristor array architectures—1T1R and 1S1R crossbar arrays—are discussed. These structures enable efficient low-power computing with excellent integrability.

Part 3: The applications of memristors in digital logic, multi-level storage, and artificial synapses in neural networks are examined. The review details how memristors emulate synaptic plasticity in biological systems, such as short-term potentiation (STP) and long-term potentiation (LTP).

In the context of ANNs, the review discusses how memristors achieve information processing and storage by modulating their resistive states. For CNNs, memristor arrays directly perform matrix operations in convolutional and fully connected layers, significantly improving computational efficiency while reducing power consumption. For SNNs, the implementation of spike-timing-dependent plasticity (STDP) and other synaptic behaviors is explored, mimicking the temporal response properties of biological neurons.

Fig. 1: Classification of memristors based on functional materials.

Fig. 2: Power consumption of various low-power memristors during synaptic plasticity

Fig. 3: Schematic of 1T1R and crossbar memristor arrays

Fig. 4: Low-power artificial synapses.

Fig. 5: Artificial neuron devices.

Fig. 6: Artificial neural network schematic

Fig. 7: Schematic of brain-inspired computing networks (CNNs, SNNs).

Conclusion and Outlook:
Low-power memristors show immense promise in neuromorphic computing, particularly in simulating biological neural networks and enhancing computational efficiency. As novel non-volatile memory devices, memristors offer advantages such as low power consumption and high integrability, making them ideal candidates for neuromorphic systems. However, challenges remain in stability, endurance, and programming precision. Future research should focus on optimizing memristor arrays, reducing leakage currents, and improving read/write performance.

The integration of emerging materials (e.g., 2D materials) and CMOS technology will further advance neuromorphic computing, particularly in edge computing and AI applications. Research should prioritize array optimization, multi-level storage, and low-power computing to drive innovations in intelligent hardware and IoT.

Citation:
Zhipeng Xia, Xiao Sun, Zhenlong Wang, Jialin Meng, Boyan Jin, Tianyu Wang. Low-Power Memristor for Neuromorphic Computing: From Materials to Applications. Nano-Micro Letters 17, 217 (2025). https://doi.org/10.1007/s40820-025-01705-4