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。