Diffractive Waveguides (Case No. 2025-012)
Innovation:
Professor Aydogan Ozcan and his team have developed diffractive waveguides designed for optimized transmission across spectral, spatial, and polarization-specific applications. These diffractive waveguides can be cascaded together to operate at any desired wavelength simultaneously. Additionally, they facilitate the propagation of desired modes with low loss and high mode purity through capabilities such as mode splitting, mode filtering, and multiplexing polarization states. By integrating diffractive layers with deep learning optimization, the waveguides allow for the periodic modulation of the phase structure of light, without the need for dispersion engineering. With the ability to effectively replicate any conventional dielectric waveguide, the invention is set to broaden the scope of current systems utilizing waveguides by providing adaptable solutions for multiple tasks at a time with enhanced signal transmission.
Potential Applications:
● Telecommunication and satellite systems
● Defense/military applications
● Imaging systems including medical diagnostics
● Spectroscopy
● Sensing toxic gases/liquids
● Optical signal transmission
● Laser technology
● Integrated photonics
● Quantum photonic circuits
Advantages:
● Adaptable to any desired wavelengths or application
● Low loss with high mode purity
● Eliminates the need for dispersion engineering
● Capable of mode filtering and mode splitting
● Optimized with deep learning
● Efficient manufacturing technique
All-Optical Phase Conjugation Using Diffractive Wavefront Processing (Case No. 2024-104)
Researchers led by Professor Aydogan Ozcan have developed an all-optical phase conjugation system able to correct phase aberrations across different parts of the electromagnetic spectrum. There is no need for lenses, mirrors, digital processing, or external power sources. The conjugation is achieved at the speed of light propagation due to the elimination of the need for digital computation and light field modulation known to slow down other digital processing methods. The system consists of a set of passive diffractive layers that were optimized using deep learning algorithms, and the effectiveness of this approach was validated using terahertz radiation on phase distortions that the model was not trained on. Applying this new all-optical phase conjugation method can improve complex imaging systems by reducing distortions and errors.
Press Release:
Researchers Introduce Partially Coherent Unidirectional Imaging Systems (CNSI)
Potential Applications:
• Computer vision
• Optical communications
• Privacy and encryption
• Augmented/virtual reality
• Adaptive optics
• Turbidity suppression
• Multispectral imaging
• Pulse shaping
• Spectral filtering
Advantages:
• Compact and scalable
• Adaptable to different parts of the electromagnetic spectrum
• No processing delay
• All-optical processing
• Low power consumption
Related Papers:
Unidirectional imaging with partially coherent light
Shen, CY., Li, J., Gan, T. et al. All-optical phase conjugation using diffractive wavefront processing. Nat Commun 15, 4989 (2024). https://doi.org/10.1038/s41467-024-49304-y
Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors (Case No. 2023-192)
Innovation:
Professor Ozcan and colleagues have developed a method for designing all-optical universal linear processors of spatially incoherent light. Their optical processors are comprised of a set of structurally engineered surfaces. These surfaces exploit the successive diffraction of light to perform linear transformations of the light field without using external digital computational power. They further show that using deep learning-based methods they can perform any arbitrary linear transformation using just the optical intensity of the light source. In addition, these optical processors can be used to perform transformations in parallel using broadband light. This potentially reduces the hardware complexity required for an optical processing system. The ability to process incoherent information optically, without reliance on electronic or digital systems, marks a significant advancement in the field of optical computing.
Potential Applications:
• Optical processors
• Visual computing systems
• Computational microscopy
• Imaging platforms
• Autonomous vehicle navigation
Advantages:
• Processing of spatially-incoherent light
• Works with phase and amplitude optical features
• High accuracy
• Compatible with any linear intensity transformation
• Can be extended to complex-valued transformations
Development-To-Date:
Researchers have evaluated the performance of this platform by using it to successfully classify handwritten digits under spatially incoherent illumination with an accuracy of over 95%.
Related Papers:
• Rahman, M., Yang, X., Li, J., Bai B., and Ozcan, A. Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Procesors. Light: Science & Applications 2023 https://www.nature.com/articles/s41377-023-01234-y
Press Release:
Universal linear processing of spatially incoherent light through diffractive optical networks
Diffractive All-Optical Computing for Quantitative Phase Imaging (Case No. 2022-174)
Innovation:
Researchers led by Aydogan Ozcan have developed an all-optical diffractive network that quantitatively reconstructs phase shift intensity from optically-transparent samples. The devised QPI network can synthesize a quantitative phase image of an object by converting the input phase information into intensity variations at the output plane. In other words, the diffractive network can reconstruct an output image based on how input light changes after passing through the sample. The solution is compact and removes the need for iterative computational methods to reconstruct images, enabling power-efficient, high frame-rate and compact phase imaging systems. This innovation can revolutionize the fields of microcopy and immunohistochemistry by removing the need for expensive computational methods and cumbersome cellular staining required for biological imaging. In addition, this solution can be applied in various other applications, including holographic displays, lighting, and illumination.
Demonstration Video:
Aydogan Ozcan - Diffractive Optical Networks & Computational Imaging Without a Computer
Potential Applications:
• On-chip microscopy
• Digital immunohistology
• Immunology
• Cell migration dynamics
• Holographic lighting and displays
Advantages:
• All-optical
• Power-efficient
• Generates quantitative outputs of transparent tissues
Related Papers:
Mengu, Deniz & Ozcan, Aydogan. (2022). Diffractive all-optical computing for quantitative phase imaging. Optics. 2022, https://doi.org/10.48550/arXiv.2201.08964
Mengu, D., Ozcan, A., All-Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging. Adv. Optical Mater. 2022, 10, 2200281. https://doi.org/10.1002/adom.202200281
Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network (Case No. 2022-230)
Innovation:
Professor Ozcan and his research team have invented a single diffractive network that is operated optically to enable multiple arbitrarily-selected linear transformation operations with complex values. In this all-optical system, the computational task is completed as the light passes through thin and passive optical elements, so the execution is performed at the speed of light, and the process does not consume power except for the illumination light. It utilizes standard isotropic diffractive materials, which make the fabrication cost-effective and scalable. Compared to traditional methods to employ multiple diffractive subsystems, this innovation applies polarization multiplexing in a single diffractive network, and integrates multiple tasks within the same system, rendering it better in terms of speed, versatility, and compactness. Additionally, the computing capacity of this diffractive network can be improved further, as the polarization multiplexing can be flexibly coupled with other multiplexing methods.
Potential Applications:
• All-optical high-throughput processors
• Machine vision computing systems
• Polarization-aware optical information processing systems
• Artificial intelligence tasks
• High-performance general-purpose processing systems
• Optical information processing (e.g., object classification & image reconstruction)
• Quantitative phase imaging
Advantages:
• Processing at the speed of light
• Low-power execution
• Simple design and common materials selection
• Easy fabrication with great scalability
• Versatile, compact, and cost-effective system
Related Papers:
Li, J., Hung, Y.C., Kulce, O., Mengu, D. and Ozcan, A., 2022. Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network. Light: Science & Applications, 11(1), pp.1-20.
Lin, X., Rivenson, Y., Yardimci, N.T., Veli, M., Luo, Y., Jarrahi, M. and Ozcan, A., 2018. All-optical machine learning using diffractive deep neural networks. Science, 361(6406), pp.1004-1008.