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
ABSTRACT With the continuous development of computer image processing, developing efficient and low‐power computing devices has become a key challenge. Memristors have integrated in‐situ storage and computing capabilities, making them an ideal choice for low‐power image processing computing architectures. However, current memristors are confronted with the dual challenges of poor stability and further reduction in power consumption. Here, we fabricated Sm:HfO 2 thin film ferroelectric memristors, which combined the excellent ferroelectric and dielectric properties of hafnium‐based ferroelectric memristors, and the doping of Sm elements further improved their electrical characteristics. The device demonstrated stable switching characteristics, good hold, and durability for 10 8 cycles, as well as an ultra‐low power consumption of 23.82 fJ. Meanwhile, a full‐hardware image edge detection computing system is constructed by building a 3 × 3 memristor array with devices, and a grayscale image is used for hardware image edge detection calculation. The test result structural similarity index measure (SSIM) is 93.04%, and the software similarity reached 98.54%. This work has reduced power consumption and improved device stability by fabricating devices with hafnium‐based ferroelectric materials doped with Sm elements, providing a solution for high efficiency and high accuracy hafnium‐based ferroelectric brain‐like computing systems.
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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