인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Terahertz (THz) metamaterials with high-figure-of-merit (high-FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low-asymmetry geometric structures, leaving high-asymmetry designs largely unexplored due to the inefficiency of trial-and-error-based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data-driven nature. Here, a novel prior knowledge-guided generative model augmented by a physics-constrained active learning mechanism to design high-asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high-FoM THz resonance and generates new high-asymmetry structures. To mitigate the limited number of classical structures, the generated high-asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high-asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning-based metamaterial design. The proposed scheme for generating high-asymmetry structures provides a new effective and efficient solution for high-FoM resonance, expanding applications in high-sensitivity THz metadevices.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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