Key Takeaways & Executive Findings
- •• Developed a novel photo-memristor with single-pulse low energy consumption (187 pJ) and multi-pulse linearity up to 0.996. • Using photoelectric synaptic characteristics, achieved long-term memory in a 3 × 3 array and over 92% activation of female facial feature recognition in a 64 × 64 model. • Using the continuous response characteristics of optical synapses, an intelligent driving system with automatic night meeting was designed. • The device demonstrates high linearity (99.6%) and low energy consumption, enabling high-precision visual perception and integration for neuromorphic vision systems.
Abstract
Optical synapses have an ability to perceive and remember visual information, making them expected to provide more intelligent and efficient visual solutions for humans. As a new type of artificial visual sensory devices, photoelectric memristors can fully simulate synaptic performance and have great prospects in the development of biological vision. However, due to the urgent problems of nonlinear conductance and high-energy consumption, its further application in high-precision control scenarios and integration is hindered. In this work, we report an optoelectronic memristor with a structure of TiN/CeO2/ZnO/ITO/Mica, which can achieve minimal energy consumption (187 pJ) at a single pulse (0.5 V, 5 ms). Under the stimulation of continuous pulses, linearity can be achieved up to 99.6%. In addition, the device has a variety of synaptic functions under the combined action of photoelectric, which can be used for advanced vision. By utilizing its typical long-term memory characteristics, we achieved image recognition and long-term memory in a 3×3 synaptic array and further achieved female facial feature extraction behavior with an activation rate of over 92%. Moreover, we also use the linear response characteristic of the device to design and implement the night meeting behavior of autonomous vehicles based on the hardware platform. This work highlights the potential of photoelectric memristors for advancing neuromorphic vision systems, offering a new direction for bionic eyes and visual automation technology.
1. Introduction
Advanced artificial vision technology plays a crucial role in areas such as smart homes, self-driving cars and humanoid robots [1–4]. These systems are usually designed using complementary metal oxide semiconductor (CMOS)-integrated circuits based on von Neumann architectures, but this design approach faces “memory wall” and “power wall” problems, which limit its development in the field of artificial intelligence. In contrast, the human visual system exhibits a high energy-efficiency ratio, which stems from the retina’s ability to instantly perceive and initially process light, coupled with parallel processing mechanisms in the brain’s visual cortex, which are able to rapidly and accurately process visual information [5, 6]. For example, synaptic plasticity in the human retina efficiently extracts key visual features [7] and reduces data redundancy, thereby accelerating processing in the visual cortex. In order to mimic this efficient biological vision mechanism, the development of an artificial vision system with sense and memory behavior is the key to realizing the bionic eyes and bionic robots [8].
Recently, artificial vision system has been paid more attention, and there are a variety of implementation methods; there are sensor connected memristor devices, photodetectors and memristors integrated, and three-terminal photoelectric devices [9–12]. The method of series memristor not only increases the complexity of system integration, but also increases the energy consumption, which is not conducive to the large-area integration of vision chips. At present, the three-terminal photoelectric transistors are more studied [10, 11], which has shown the feasibility of neuromorphic vision systems. But due to structural problems, the array density is low and the circuit design is complex, which may lead to low processing speed and high energy consumption [13]. As a new neuromorphic vision device, two-terminal photoelectric memristors can realize the way of processing light information like human eyes and complete the perception, memory and processing of signals [13–16]. More importantly, the two-terminal photoelectric memristors are more conducive to realizing the high density integration of simple cross-point arrays of ultra-high-resolution vision chips [17]. However, although two-terminal photoelectric memristor have great potential in the field of visual information processing, it is difficult for most devices to achieve multistage linear conductance modulation at low energy consumption [18–24], which will limit further applications of devices in high-precision visual perception [25, 26]. Because linear conductance modulation optimizes each pulse increment [26], allowing the photoelectric memristor to be precisely programmed to the target conductance.
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Zhenyu Zhou, Zixuan Zhang, Pengfei Li, Zhiyuan Guan, Yuchen Li, Xiaoxu Li, Shan Xu, Jianhui Zhao, Xiaobing Yan (2025). Low Energy Consumption Photoelectric Memristors with Multi-Level Linear Conductance Modulation in Artificial Visual Systems Application. Nano-Micro Letters. https://doi.org/10.1007/s40820-025-01816-y
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Frequently Asked Questions
What is the energy consumption of the photoelectric memristor?
The photoelectric memristor achieves minimal energy consumption of 187 pJ per single pulse (0.5 V, 5 ms).
What is the linearity of the device under continuous pulses?
Under continuous pulse stimulation, the device achieves linearity up to 99.6%.
What applications are demonstrated using the photoelectric memristor?
The device is used for image recognition and long-term memory in a 3×3 synaptic array, female facial feature extraction with over 92% activation rate, and an intelligent driving system with automatic night meeting behavior.
What is the device structure of the photoelectric memristor?
The device structure is TiN/CeO2/ZnO/ITO/Mica.
How does the photoelectric memristor contribute to artificial visual systems?
It simulates synaptic performance with low energy consumption and high linearity, enabling high-precision visual perception and integration for neuromorphic vision systems, offering a new direction for bionic eyes and visual automation technology.
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