Quantitative phase imaging (QPI) is a label-free technique that provides
optical path length information for transparent specimens, finding utility in
biology, materials science, and engineering. Here, we present quantitative
phase imaging of a 3D stack of phase-only objects using a
wavelength-multiplexed diffractive optical processor. Utilizing multiple
spatially engineered diffractive layers trained through deep learning, this
diffractive processor can transform the phase distributions of multiple 2D
objects at various axial positions into intensity patterns, each encoded at a
unique wavelength channel. These wavelength-multiplexed patterns are projected
onto a single field-of-view (FOV) at the output plane of the diffractive
processor, enabling the capture of quantitative phase distributions of input
objects located at different axial planes using an intensity-only image sensor.
Based on numerical simulations, we show that our diffractive processor could
simultaneously achieve all-optical quantitative phase imaging across several
distinct axial planes at the input by scanning the illumination wavelength. A
proof-of-concept experiment with a 3D-fabricated diffractive processor further
validated our approach, showcasing successful imaging of two distinct phase
objects at different axial positions by scanning the illumination wavelength in
the terahertz spectrum. Diffractive network-based multiplane QPI designs can
open up new avenues for compact on-chip phase imaging and sensing devices.

基于深度学习训练的多程面量化相位成像设计可为紧凑型芯片相位成像和传感器设备开辟新的途径。

利用波长复用的衍射光学处理器进行多平面定量相位成像

Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed  Diffractive Optical Processor

Complex field imaging, which captures both the amplitude and phase
information of input optical fields or objects, can offer rich structural
insights into samples, such as their absorption and refractive index
distributions. However, conventional image sensors are intensity-based and
inherently lack the capability to directly measure the phase distribution of a
field. This limitation can be overcome using interferometric or holographic
methods, often supplemented by iterative phase retrieval algorithms, leading to
a considerable increase in hardware complexity and computational demand. Here,
we present a complex field imager design that enables snapshot imaging of both
the amplitude and quantitative phase information of input fields using an
intensity-based sensor array without any digital processing. Our design
utilizes successive deep learning-optimized diffractive surfaces that are
structured to collectively modulate the input complex field, forming two
independent imaging channels that perform amplitude-to-amplitude and
phase-to-intensity transformations between the input and output planes within a
compact optical design, axially spanning ~100 wavelengths. The intensity
distributions of the output fields at these two channels on the sensor plane
directly correspond to the amplitude and quantitative phase profiles of the
input complex field, eliminating the need for any digital image reconstruction
algorithms. We experimentally validated the efficacy of our complex field
diffractive imager designs through 3D-printed prototypes operating at the
terahertz spectrum, with the output amplitude and phase channel images closely
aligning with our numerical simulations. We envision that this complex field
imager will have various applications in security, biomedical imaging, sensing
and material science, among others.

设计了一种基于强度传感器阵列的复杂场成像器，并通过使用迭代相位恢复算法提供幅值和定量相位信息，可用于安全、生物医学成像、传感和材料科学等多个领域。

全光复杂场成像使用衍射处理器

All-optical complex field imaging using diffractive processors

We introduce an all-optical Diffractive Deep Neural Network (D2NN)
architecture that can learn to implement various functions after deep
learning-based design of passive diffractive layers that work collectively. We
experimentally demonstrated the success of this framework by creating
3D-printed D2NNs that learned to implement handwritten digit classification and
the function of an imaging lens at terahertz spectrum. With the existing
plethora of 3D-printing and other lithographic fabrication methods as well as
spatial-light-modulators, this all-optical deep learning framework can perform,
at the speed of light, various complex functions that computer-based neural
networks can implement, and will find applications in all-optical image
analysis, feature detection and object classification, also enabling new camera
designs and optical components that can learn to perform unique tasks using
D2NNs.

使用光学全衍射深度神经网络 (D2NN) 进行手写数字分类和成像透镜功能的学习，可以在光速下执行复杂函数，该技术具有潜在的光学图像分析和对象分类应用，并能够实现独特的任务。