Neuromorphic devices based on semiconductor nanomaterials: mechanisms, applications and challenges

Shiyang Zhang

doi:10.54254/2977-3903/2025.29136

Research Article

Open Access

Neuromorphic devices based on semiconductor nanomaterials: mechanisms, applications and challenges

Shiyang Zhang ^1*

¹ Material Science, Beijing University of Technology

^*Corresponding author: zhangshiyang@emails.bjut.edu.cn

Published on 31 October 2025

AEI Vol.16 Issue 10

ISSN (Print): 2977-3911

ISSN (Online): 2977-3903

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Abstract

The traditional von Neumann computing architecture is facing bottlenecks such as high power consumption and weak parallel processing capabilities, making it difficult to meet the demands of efficient computing for artificial intelligence and complex information processing. Neuromorphic devices that imitate the structure and function of biological neural systems have become a breakthrough direction. Semiconductor nanomaterials, with their quantum size effect, high specific surface area, and excellent electrical tunability, provide core support for building more efficient and smaller brain-like devices. This paper systematically expounds the characteristics of typical semiconductor nanomaterials such as quantum dots, nanowires, and two-dimensional semiconductors, and deeply analyzes their mechanism of enhancing device performance by simulating neuronal signal transmission and synaptic plasticity (such as conductance regulation based on surface effects). It reveals the key value of the small size effect of materials in improving device integration and reducing energy consumption. Combined with application cases such as neuromorphic vision sensors, neuromorphic computing chips, and biomedical neural interfaces, it verifies the significant advantages of semiconductor nanomaterial-based neuromorphic devices in response speed (microsecond level), energy consumption (nanowatt level), and integration density. Research shows that semiconductor nanomaterials, through structural design and functional regulation, can break through the performance limitations of traditional devices, providing an important technical path for the development of the next generation of low-power, high-integration brain-like computing systems, and are of great significance for promoting innovation in fields such as artificial intelligence and brain-computer interfaces.

Keywords:

semiconductors, nanometre, chip, neural networks

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References

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References

[6]. Heng Liu, Miao Ji, Yufei Liu, et al. (2025) Research Progress on Brain-like Learning Characteristics of One-dimensional Memristive Synaptic Devices [J].Electronic Technology,54(01): 7-9.

[7]. Sun Chi. (2024) Hydrogen Response Characteristics of Spin Heterojunctions and Simulation Study of Nanoscopic Oscillators [D]. University of Electronic Science and Technology of China.

[9]. Bingyan Yi. (2025) Research and Application of Visual Bionic Model Based on Deep Spiking Neural Networks [D]. University of Electronic Science and Technology of China.

[11]. Xiaoyan Xu. (2020). Characteristics of the Biological Corona of Nanoparticles and Its Effect on Heavy Metal Adsorption by Nanoparticles [D]. Xiangtan University.

Cite this article

Zhang,S. (2025). Neuromorphic devices based on semiconductor nanomaterials: mechanisms, applications and challenges. Advances in Engineering Innovation,16(10),61-66.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Journal: Advances in Engineering Innovation

Volume number: Vol.16

Issue number: Issue 10

ISSN: 2977-3903(Print) / 2977-3911(Online)

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