Key Takeaways & Executive Findings
- •• A novel GaN/AlN-based ultrathin quantum-disks-in-nanowires sensor was fabricated, demonstrating voltage bias tunable response characteristics to light stimuli. • Image enhancement functionality and a robust reservoir computing system were demonstrated based on the voltage tunable long-term and short-term persistent photocurrent respectively. • A high-performance artificial vision system with the two integrated functions was demonstrated, achieving a remarkable improvement in human action recognition. • The synergistic interaction of two photoresponse modes within a single device increased HAR recognition accuracy from 51.4% to 81.4%.
Abstract
Human action recognition (HAR) is crucial for the development of efficient computer vision, where bioinspired neuromorphic perception visual systems have emerged as a vital solution to address transmission bottlenecks across sensor-processor interfaces. However, the absence of interactions among versatile biomimicking functionalities within a single device, which was developed for specific vision tasks, restricts the computational capacity, practicality, and scalability of in-sensor vision computing. Here, we propose a bioinspired vision sensor composed of a GaN/AlN-based ultrathin quantum-disks-in-nanowires (QD-NWs) array to mimic not only Parvo cells for high-contrast vision and Magno cells for dynamic vision in the human retina but also the synergistic activity between the two cells for in-sensor vision computing. By simply tuning the applied bias voltage on each QD-NW-array-based pixel, we achieve two biosimilar photoresponse characteristics with slow and fast reactions to light stimuli that enhance the in-sensor image quality and HAR efficiency, respectively. Strikingly, the interplay and synergistic interaction of the two photoresponse modes within a single device markedly increased the HAR recognition accuracy from 51.4% to 81.4% owing to the integrated artificial vision system. The demonstration of an intelligent vision sensor offers a promising device platform for the development of highly efficient HAR systems and future smart optoelectronics.
1. Introduction
In the era of technological revolution, human action recognition (HAR) technology, characterized by artificial intelligence, has become increasingly important in various applications, including security surveillance, video retrieval, human–computer interaction, and autonomous navigation [1–7]. However, HAR from video sequences still faces challenges, such as background clutter, partial occlusion, variations in scale or viewpoint, lighting conditions, and appearance changes [8–10]. To date, long short-term memory (LSTM) architectures have been successfully applied to analyze temporal complex human activity data because of the recurrent connections in their hidden layers [11–13]. However, these approaches also have several drawbacks, such as the need for large datasets and the time- and energy-consuming training process [14].
In contrast, biological vision systems can efficiently and autonomously perceive motion-related information, performing image enhancement and classification tasks in real time through the coordinated operation of various retinal cells, including photoreceptors, bipolar cells, and ganglion cells [15–18]. Notably, retinal ganglion cells can be categorized into two types: magnocellular (Magno) and parvocellular (Parvo). Magnocellular cells respond rapidly in motion detection scenarios, corresponding to “short-term” functionality, whereas parvocellular cells exhibit a slower response in low-contrast environments, corresponding to “long-term” functionality [19–21]. Consequently, the human visual system exhibits remarkable adaptability to a wide range of environmental conditions—whether static or dynamic, and from blurred to well-defined stimuli—enabled by biologically inspired long short-term memory (bio-LSTM) architectures. This functional differentiation among retinal cells underpins the efficiency of natural vision and serves as inspiration for the development of dual-functional artificial vision devices.
Herein, we propose a versatile vision sensor composed of GaN/AlN-based ultrathin quantum-disks-in-nanowires (QD-NWs) with reconfigurable photoelectric properties to mimic visual behaviors in biological cells. Notably, the well-designed nanowire consists of an n-GaN layer, GaN/AlN multiple QDs, and an n-GaN cap layer. The n–i–n-type band structure minimizes the separation between electrons and holes, whereas the high barrier height of the AlN quantum barriers confines carriers in the QD-NWs, enabling a dual-modal persistent photocurrent (PPC). Furthermore, each quantum disk comprises several layers of GaN, which enhances ...
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Zhixiang Gao, Xin Ju, Huabin Yu, Wei Chen, Xin Liu, Yuanmin Luo, Yang Kang, Dongyang Luo, JiKai Yao, Wengang Gu, Muhammad Hunain Memon, Yong Yan, Haiding Sun (2026). Ultrathin Gallium Nitride Quantum-Disk-in-Nanowire-Enabled Reconfigurable Bioinspired Sensor for High-Accuracy Human Action Recognition. SinoTechIntel Verified Research. https://doi.org/10.1007/s40820-025-01888-w
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Frequently Asked Questions
What is the main innovation of this sensor?
The sensor uses GaN/AlN quantum-disks-in-nanowires to mimic both Parvo and Magno cells of the human retina, with voltage-tunable photoresponse enabling dual-modal functionality for image enhancement and dynamic vision.
How does the sensor improve human action recognition accuracy?
By integrating two photoresponse modes (slow and fast) within a single device, the sensor leverages synergistic interactions to boost HAR accuracy from 51.4% to 81.4%.
What are the key features of the QD-NW structure?
The nanowire consists of an n-GaN layer, GaN/AlN multiple quantum disks, and an n-GaN cap layer, with an n-i-n band structure that minimizes electron-hole separation and high AlN barriers for carrier confinement.
What applications could this sensor enable?
The sensor is promising for efficient HAR systems, smart optoelectronics, and in-sensor vision computing, potentially impacting security surveillance, human-computer interaction, and autonomous navigation.
How does the sensor achieve voltage-tunable response?
By applying different bias voltages to each pixel, the sensor can switch between slow (long-term) and fast (short-term) photoresponse characteristics, enabling reconfigurable functionality.
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