KAIST

< (From left) Ph.D. student Hanbin Cho, Postdoctoral Researcher Wenxuan Zhu, Professor Joonki Suh, and MS-PhD integrated student Changhwan Kim > 

A technology that surpasses the limitations of existing sensors, which failed to distinguish between water and asphalt on dark roads, has emerged to enhance the accuracy of autonomous driving and medical diagnostics. Our university's research team has developed a next-generation polarization sensor that can read the "direction" of light and change its own response. KAIST announced on May 12th that a research team led by Professor Joonki Suh from the Department of Chemical and Biomolecular Engineering has developed a "self-reconfigurable" polarization sensor array technology that regulates its operation by finding the optimal state using "polarization" information—the property of light vibrating in a specific direction. With the recent explosive increase in data and the rapid development of artificial intelligence technology, the need for next-generation vision systems that can efficiently process vast amounts of information with low energy is growing. However, existing image sensors only detect the intensity (brightness) of light, limiting their ability to precisely grasp the orientation or surface structure of objects. To overcome these limitations, the research team developed a polarization-based sensor technology capable of recognizing the vibration direction of light. In particular, by utilizing a "heterostructure" that combines two different materials—tellurium (Te) and rhenium disulfide (ReS₂)—they effectively implemented characteristics where the response to light varies depending on the crystal orientation.

< Conceptual diagram of self-reconfigurable polarization sensor and in-sensor computing based on dual-anisotropy vdW heterostructures > 

To precisely stack the two materials so they cross each other, the research team applied "Epitaxial Atomic Layer Deposition," a process that controls crystal structures by stacking materials precisely at the atomic layer level. By ensuring the crystal structures of the two materials interlock accurately, they secured higher reproducibility and stable performance compared to previous methods. In this structure, when light is irradiated, interfacial carrier transfer and trapping (a phenomenon where electrons move or stay at specific locations) occur at the material boundary. As a result, a "bipolar photoresponse"—a light-induced reaction where the current direction flips depending on conditions such as light intensity, wavelength, and direction—appears. A key feature is that the sensor's operating state can be freely adjusted using only light, without external electrical signals. Furthermore, this technology can be applied to "in-sensor computing" structures where the sensor itself processes data, allowing for the efficient processing of multi-dimensional optical information that changes over time without complex calculation processes. In actual experiments, it recorded a high accuracy of over 95% in recognizing moving objects, proving its potential for applications in various fields such as autonomous driving and medical diagnosis.

< Experimental image of a polarization AI sensor platform capable of light-based operational reconfiguration (AI-generated image) > 

Professor Joonki Suh stated, "This research presents a new foundation for AI vision technology that can secure richer visual information by utilizing polarization information. It is expected to play an important role in implementing low-power, high-efficiency AI systems in the future." Wenxuan Zhu (Postdoctoral Researcher) and Changhwan Kim (Ph.D. student) participated as first authors in this study, with Professor Joonki Suh participating as the corresponding author. The research results were published on April 14 in the international academic journal Nature Sensors.

• Paper Title: Self-reconfigurable polarization perception in dual-anisotropy heterostructures for high-dimensional in-sensor computing
• Authors: Wenxuan Zhu, Changhwan Kim, Ruofan Zhang, Mingchun Lu, Namwook Hur, Hanbin Cho, Jihyun Kim, Jiacheng Sun, Joohoon Kang, Junchi Yan, Yuan Cheng & Joonki Suh
• DOI: https://doi.org/10.1038/s44460-026-00057-9

< Paper portfolio and QR code > 

Meanwhile, this research was conducted with the support of the PIM AI Semiconductor Core Technology Development (Device) Project and the Individual Basic Research Project of the National Research Foundation of Korea, funded by the Ministry of Science and ICT, and the Industrial Innovation Talent Growth Support Project of the Korea Institute for Advancement of Technology (KIAT).