基于深度学习的单拍计算光谱仪使用多层薄膜
David S Bhatti1, Jioh Lee2, Cheolsun Kim3
1Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
Scientific reports
|July 1, 2025
概括
这项研究介绍了用于移动使用的深度学习计算光谱仪. 它从一次性测量中准确地重建狭窄和宽谱,使现场检测和自我诊断成为可能.
科学领域:
- 光学和光子学 在光学和光子学.
- 频谱学是一种光谱学.
- 机器学习应用 机器学习应用
背景情况:
- 计算光谱仪由于其尺寸和速度,为移动应用提供了潜力.
- 以前的演示仅限于简单的光谱,阻碍了更广泛的应用.
研究的目的:
- 开发一个基于深度学习 (DL) 的单射计算光谱仪.
- 为了实现对移动传感的狭窄和宽谱的重建.
主要方法:
- 使用晶圆级别的 stencil lithography 制造一个多层薄膜过器阵列.
- 过器阵列与CMOS图像传感器集成,用于单次曝光图像捕获.
- 开发DL架构 (密集层,U-Net骨干与剩余连接) 用于频谱重建.
主要成果:
- 精确重建323个测试光谱 (500-850纳米,1纳米间隔) 以0.0288.8的低根平均平方误差.
- 通过SEM验证多层薄膜过器,确认均沉积和高产量.
- 一个紧,快速和准确的计算光谱仪的演示,与CMOS传感器兼容.
结论:
- 开发的基于DL的计算光谱仪有效地从单次测量中重建复杂的光谱.
- 该设备的性能和功能使其适合在移动应用中进行商业化.
- 这项技术推进了便携式光谱分析,用于现场检测和自我诊断.
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