Ultraviolet CMOS image sensor for environment analysis via energy-down-shift mechanism of blue-light emitting quantum dots
- Authors
- Jeong, Ui-Hyun; Park, Jea-Gun
- Issue Date
- Sep-2024
- Publisher
- 한국물리학회
- Keywords
- Quantum dot; CMOS image sensor; Ultraviolet
- Citation
- Journal of the Korean Physical Society, v.85, no.6, pp 482 - 489
- Pages
- 8
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- Journal of the Korean Physical Society
- Volume
- 85
- Number
- 6
- Start Page
- 482
- End Page
- 489
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211195
- DOI
- 10.1007/s40042-024-01132-5
- ISSN
- 0374-4884
1976-8524
- Abstract
- Recently, there has been a global societal focus on the management of air pollution. Measurements of air pollution are conducted using various methods depending on the pollutants, including ultraviolet (UV) absorption methods for ozone (O3) and fluorescence methods for sulfur dioxide (SO2). However, the conventional silicon-based complementary metal-oxide-semiconductor image sensor (Si-CIS) is not suitable for UV measurements due to the low quantum efficiency (QE) of silicon for UV light. Consequently, different types of detection sensors are used for different air pollutants, leading to limitations in measurement locations and resulting in errors depending on the installation position. To address these limitations, we propose a quantum dot complementary metal-oxide-semiconductor image sensor (QD-CIS) capable of imaging UV light using the energy-down-shift (EDS) mechanism of quantum dots (QDs). The synthesized QDs absorb light at UV wavelengths, convert it into visible blue light through EDS, and emit luminescence. The converted intensity allows the detection of UV intensity by the CIS. Through the designed QD-CIS and UV LED illumination, we measured the sensitivity to changes in the concentrations of the representative air pollutants NO2 and SO2. The results showed a sensitivity increase of 6.83 times for NO2 and 21.39 times for SO2 compared to conventional CIS. This suggests the potential of UV imaging to overcome these limitations using existing CIS components.
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