[Paper Review] All-Optical Phase Conjugation Using Diffractive Wavefront Processing

Che‐Yung Shen, Jingxi Li+3 more|arXiv (Cornell University)|Nov 8, 2023

Advanced Optical Imaging Technologies54 references4 citations

TL;DR

This paper presents a deep learning-optimized, passive diffractive wavefront processor that enables all-optical phase conjugation (OPC) for coherent fields with phase aberrations. By 3D-fabricating diffractive layers trained to conjugate wavefronts, the system achieves robust OPC in the terahertz band across unseen distortions, demonstrating a compact, scalable solution for aberration correction and turbidity suppression without active components.

ABSTRACT

Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with various applications ranging from imaging to beam focusing. Here, we present the design of a diffractive wavefront processor to approximate all-optical phase conjugation operation for input fields with phase aberrations. Leveraging deep learning, a set of passive diffractive layers was optimized to all-optically process an arbitrary phase-aberrated coherent field from an input aperture, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated the efficacy of this wavefront processor by 3D fabricating diffractive layers trained using deep learning and performing OPC on phase distortions never seen by the diffractive processor during its training. Employing terahertz radiation, our physical diffractive processor successfully performed the OPC task through a shallow spatially-engineered volume that axially spans tens of wavelengths. In addition to this transmissive OPC configuration, we also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and scalable nature, our diffractive wavefront processor can be used for diverse OPC-related applications, e.g., turbidity suppression and aberration correction, and is also adaptable to different parts of the electromagnetic spectrum, especially those where cost-effective wavefront engineering solutions do not exist.

Motivation & Objective

• To develop a passive, compact, and scalable solution for all-optical phase conjugation (OPC) in wavefront correction.
• To overcome limitations of nonlinear OPC methods by enabling all-optical processing using engineered diffractive structures.
• To extend OPC capabilities to the terahertz band, where cost-effective wavefront engineering remains challenging.
• To validate the system's robustness by demonstrating OPC on phase distortions not seen during training.
• To enable applications in turbidity suppression and aberration correction through a physically realizable, passive device.

Proposed method

• A deep learning framework was used to optimize a sequence of passive diffractive layers to perform all-optical phase conjugation on input coherent fields.
• The diffractive layers were trained to transform an input wavefront with arbitrary phase aberrations into its complex conjugate at the output.
• The trained diffractive processor was physically realized via 3D fabrication of the optimized phase profiles.
• The system was experimentally validated using terahertz radiation, with axial thickness spanning tens of wavelengths.
• A diffractive phase-conjugate mirror was created by combining the optimized diffractive layers with a standard reflective mirror.
• The approach enables wavefront processing without active components, relying solely on passive diffraction.

Experimental results

Research questions

• RQ1 Can a passive, diffractive wavefront processor trained via deep learning perform all-optical phase conjugation on arbitrary phase-aberrated inputs?
• RQ2 How well does the diffractive processor generalize to phase distortions not present during training?
• RQ3 Can this approach be implemented in the terahertz band with a physically compact, 3D-fabricated structure?
• RQ4 What is the performance of the diffractive processor in correcting wavefront distortions for applications like aberration correction and turbidity suppression?
• RQ5 How scalable and adaptable is the diffractive wavefront processor across different regions of the electromagnetic spectrum?

Key findings

• The 3D-fabricated diffractive wavefront processor successfully performed all-optical phase conjugation on phase distortions not encountered during training.
• The system achieved effective wavefront correction using a physically compact, transmissive structure that spans tens of wavelengths axially.
• The diffractive processor demonstrated robustness in correcting complex phase aberrations in the terahertz band with high fidelity.
• The integration of deep learning-optimized diffractive layers with a standard mirror enabled a functional diffractive phase-conjugate mirror.
• The approach is scalable and adaptable to various electromagnetic bands, especially those lacking cost-effective wavefront engineering solutions.
• The method enables passive, low-power wavefront correction without requiring nonlinear optical materials or active feedback loops.