Materials, design, and fabrication of curved image sensors for bio-inspired, AI-friendly vision systems
2026 · International Journal of Extreme Manufacturing
저자
Hyeonseung Choi, Ji‐Eun Yeo, Sehui Chang, Changsoon Choi, Young Min Song, 김대형 (Dae‐Hyeong Kim)
연구주제
Advanced optical system design
NTIS 과학기술표준분류
가장 가까운 분류(기계 > 로봇/자동화기계 > 로봇 비전/생산자동화기술)가 화학·화공 범위 밖이다. 배정 근거: AI 키워드가 잡은 NTIS 과제의 분류.
AI 연구 키워드·방법
키워드 (OpenAlex)
robot vision mobile robots CMOS image sensor materials engineering
초록
Abstract Robotic vision serves as a crucial sensing modality in perception for artificial intelligence (AI), enabling mobile robots to recognize their environments with human-level intelligence. However, conventional complementary metal–oxide–semiconductor (CMOS) image sensors are not ideal for mobile robotic vision, because their planar form factor necessitates bulky and heavy optical assemblies and power- and time-intensive corrective computations. Bio-inspired vision systems, enabled by the advent of curved image sensors, provide optimal solutions for mobile robotics, enabling AI-friendly imaging functionalities without compromising mobility. In this review, we summarize recent advances in bio-inspired vision systems, with a special focus on curved image sensors and their biologically motivated implementations in robotic vision systems. We first introduce fabrication strategies for cu…
이 논문을 지원한 국가R&D 과제 (NTIS 성과)
NTIS 성과DB(2020–24)에서 DOI 매칭 없음
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