The core of autonomous vehicles lies in sensors that analyze surrounding road conditions. A research team led by Professor Dae Sung Chung (CE) and graduate student Sangjun Lee (CE) has produced sensors that are 100 times cheaper than existing ones, contributing to the future mass adoption of autonomous vehicles.

  The team has developed an organic photodiode (OPD) sensor that analyzes shortwave infrared rays by polaron engineering. Polarons are quasiparticles that form in solid-state materials due to the coupling of excess electrons. The study was published in the journal Advanced Materials.

  The Light Detection and Ranging (LiDAR), often referred to as the eyes of autonomous vehicles, utilizes light to analyze information such as the location of obstacles. Operating with infrared light, which has a longer wavelength than visible light, LiDAR is less affected by vapor and dust. This allows the sensor to identify objects accurately, even in foggy conditions.

  Existing infrared sensors using inorganic photodiodes are expensive, limiting their practicality. On the other hand, using organic materials for infrared sensors can reduce costs. Still, they are associated with drawbacks such as limited performance in the analysis of infrared signals due to excessive current within the OPD.

  To overcome these challenges, the team has designed a doping process. Doping is a process where different atoms or molecules are added to enhance the electrical properties of a photodetector. Through doping, polarons are generated within the OPD. A widely accepted theory is that when polarons are formed, it is either bound polaron or free polaron. The bound polarons have negligible effect on the OPD. On the other hand, the free polarons enhance the electrical conductivity within the OPD, allowing for a better flow of current.

  The team successfully applied the new doping process to control the conversion between bound and free polarons. As a result, they developed an OPD for analyzing shortwave infrared rays, achieving approximately a 100-fold improvement in analytical performance compared to existing models. Furthermore, the sensor successfully detected infrared beyond 1,500 nanometers.

  Prof. Chung stated, “OPD will enable the recognition of surrounding road conditions even in unfavorable weather, and it is cost-effective. It is beneficial for autonomous vehicles and contributes to the development in various fields, including augmented and virtual reality devices utilizing 3D sensors and machine vision.”