A South Korean research team has developed an ultra-low-noise organic photodetector (OPD) that delivers stable, high-sensitivity imaging in dense fog, marking a significant advance in visibility enhancement technology.
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Technologies that enable reliable visual recognition in low-visibility environments are gaining traction across sectors like autonomous driving, aviation, and smart transportation. Dense fog continues to pose serious challenges on highways, mountain roads, and airport runways, where vision-based systems often falter.
Conventional solutions, including visible light cameras, LiDAR, and thermal infrared (IR) sensors, suffer from a significant drop in signal-to-noise ratio (SNR) under scattering conditions, leading to inconsistent object and pedestrian detection.
To address this, researchers are turning to near-infrared (NIR) sensors capable of delivering stable, low-noise performance in real-world environments.
The Korea Institute of Science and Technology (KIST) has announced a breakthrough by a research team led by Dr. Min-Chul Park at the Center for Quantum Technology.
In collaboration with Prof. Jae Won Shim of Korea University and Profs. Jea Woong Jo and Sae Youn Lee of Dongguk University, the team has developed a high-sensitivity OPD that retains ultra-low noise operation even in light-scattering environments. The researchers successfully reconstructed transmission images in fog and smoke simulations and quantitatively validated the sensor’s performance.
What sets this work apart is that it's the first experimental demonstration of a hardware-based visibility enhancement system operating in realistic fog-like conditions. This follows the team’s earlier development of an AI-based fog removal software that earned a CES 2025 Innovation Award.
Building on both developments, the researchers are now moving toward an integrated software-hardware solution for enhanced visibility, aimed at applications such as autonomous vehicles, smart infrastructure, and drone-based monitoring.
A key component of the new OPD is a self-assembled monolayer electronic blocking layer known as 3PAFCN. Designed with a deep HOMO energy level and high surface energy, 3PAFCN effectively suppresses dark current and reduces interfacial charge traps.
This structural feature significantly improves device stability and responsiveness. As a result, the OPD achieved a noise current of just 2.18 fA and recorded the highest detectivity among NIR OPDs in its class, outperforming commercial silicon-based photodetectors and showing strong potential for real-world deployment.
To test its performance, the team created a lab environment simulating dense fog and conducted single-pixel imaging experiments using the OPD. Even under conditions where traditional visible-light sensors failed, the OPD captured optical signals and reconstructed object outlines, demonstrating its effectiveness in low-light, low-visibility scenarios.
This ultra-low-noise organic light sensor enables precise obstacle detection even in dense fog, making it ideal for vision-assisted systems in autonomous driving, medical imaging, and security. Its compatibility with flexible substrates and low power consumption allows deployment across various platforms, from vehicle exteriors and road infrastructure to drones and smart traffic systems-overcoming the limitations of conventional sensors.
Dr. Min-Chul Park, Principal Research Scientist, Korea Institute of Science and Technology
Journal Reference:
Oh, S., et al. (2025) Robust Imaging through Light-Scattering Barriers via Energetically Modulated Multispectral Organic Photodetectors. Advanced Materials. doi.org/10.1002/adma.202503868