单层
分类器(UML)
材料科学
光电子学
人工智能
光学
计算机科学
模式识别(心理学)
物理
纳米技术
作者
Rui Xia,Wenxiong Lin,Jin Tao,Ming Zhao,Zhenyu Yang
出处
期刊:Optics Letters
[The Optical Society]
日期:2024-05-01
卷期号:49 (9): 2505-2505
摘要
Diffractive deep neural networks, known for their passivity, high scalability, and high efficiency, offer great potential in holographic imaging, target recognition, and object classification. However, previous endeavors have been hampered by spatial size and alignment. To address these issues, this study introduces a monolayer directional metasurface, aimed at reducing spatial constraints and mitigating alignment issues. Utilizing this methodology, we use MNIST datasets to train diffractive deep neural networks and realize digital classification, revealing that the metasurface can achieve excellent digital image classification results, and the classification accuracy of ideal phase mask plates and metasurface for phase-only modulation can reach 84.73% and 84.85%, respectively. Despite a certain loss of degrees of freedom compared to multi-layer phase mask plates, the single-layer metasurface is easier to fabricate and align, thereby improving spatial utilization efficiency.
科研通智能强力驱动
Strongly Powered by AbleSci AI