# All-Optical Phase Conjugation Using Diffractive Wavefront Processing

arXiv: https://arxiv.org/abs/2311.04473 · HTML: https://mlsanity.com/paper/2311.04473/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 70 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Misalignment Resilient Diffractive Optical Networks](https://mlsanity.com/paper/2005.11464/) (2020)
- [Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks](https://mlsanity.com/paper/1909.06553/) (2019)
- [All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces](https://mlsanity.com/paper/2108.09833/) (2021)
- [Diffractive all-optical computing for quantitative phase imaging](https://mlsanity.com/paper/2201.08964/) (2022)
- [To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects](https://mlsanity.com/paper/2205.13122/) (2022)
- [Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light](https://mlsanity.com/paper/2107.06586/) (2021)
- [Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors](https://mlsanity.com/paper/2303.13037/) (2023)
- [Snapshot Multispectral Imaging Using a Diffractive Optical Network](https://mlsanity.com/paper/2212.05217/) (2022)
- [Data class-specific all-optical transformations and encryption](https://mlsanity.com/paper/2212.12873/) (2022)
- [Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy](https://mlsanity.com/paper/1906.03417/) (2019)
- [Unidirectional Imaging using Deep Learning-Designed Materials](https://mlsanity.com/paper/2212.02025/) (2022)
- [Computer-free, all-optical reconstruction of holograms using diffractive networks](https://mlsanity.com/paper/2107.08177/) (2021)
- [Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network](https://mlsanity.com/paper/2301.07908/) (2023)
- [Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network](https://mlsanity.com/paper/2308.02952/) (2023)
- [Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network](https://mlsanity.com/paper/2208.10362/) (2022)
- [Online learning of the transfer matrix of dynamic scattering media: wavefront shaping meets multidimensional time series](https://mlsanity.com/paper/2210.04033/) (2022)

## Built on by (most cited first, 8 of 8)

- [All-optical complex field imaging using diffractive processors](https://mlsanity.com/paper/2401.16779/) (2024)
- [Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor](https://mlsanity.com/paper/2403.11035/) (2024)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images](https://mlsanity.com/paper/2410.17557/) (2024)
- [Structural Vibration Monitoring with Diffractive Optical Processors](https://mlsanity.com/paper/2506.03317/) (2025)
- [Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning](https://mlsanity.com/paper/2601.18219/) (2026)
- [Wavelength-multiplexed massively parallel diffractive optical information storage and image projection](https://mlsanity.com/paper/2604.02624/) (2026)
- [Beyond the Thin-Layer Limit: Differentiable Volumetric Training for Visible-Range Diffractive Neural Networks](https://mlsanity.com/paper/2606.07896/) (2026)

## Abstract (arXiv)

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.