Lifeboat Foundation

Furthermore, many experimental factors, such as fabrication errors and physical misalignments, can affect the performance of diffractive processors during the experimental deployment stage. Investigating the inherent robustness of different nonlinear encoding strategies to such imperfections, as well as their integration with vaccination-based training strategies³⁹ or in situ training methods⁴⁰, would provide more comprehensive guidance on the implementation and limitations of these approaches. These considerations would be crucial for future research and practical implementations of diffractive optical processors.

Throughout the manuscript, our analyses assumed that diffractive optical processors consist of several stacked diffractive layers interconnected through free-space light propagation, as commonly used in the literature^10,13,41,42. Our forward model employs the angular spectrum method for light propagation, a broadly applicable technique known for its accuracy, covering all the propagating modes in free space. While our forward model does not account for multiple reflections between the diffractive layers, it is important to note that such cascaded reflections are much weaker than the transmitted light and, thus, have a negligible impact on the optimization process. This simplification does not compromise the model’s experimental validity since a given diffractive model also acts as a 3D filter for such undesired secondary sources that were ignored in the optimization process; stated differently, a by-product of the entire optimization process is that the resulting diffractive layers collectively filter out some of these undesired sources of secondary reflections, scattering them outside the output FOV. The foundation of our model has been extensively validated through various experiments^{10,11,16,18,43}, providing a good match to the corresponding numerical model in each case, further supporting the accuracy of our forward model and diffractive processor design scheme.

Finally, our numerical analyses were conducted using coherent monochromatic light, which has many practical, real-world applications such as holographic microscopy and sensing, laser-based imaging systems, optical communications, and biomedical imaging. These applications, and many others, benefit from the precise control of the wave information carried by coherent light. In addition to coherent illumination, diffractive optical processors can also be designed to accommodate temporally and spatially incoherent illumination. By optimizing the layers for multiple wavelengths of illumination, a diffractive processor can be effectively designed to operate under broadband illumination conditions^{18,19,29,43,44,45,46,47}. Similarly, by incorporating spatial incoherence into the forward model simulations, we can design diffractive processors that function effectively with spatially incoherent illumination^30,48. Without loss of generality, our current study focuses on coherent monochromatic light to establish a foundational understanding of nonlinear encoding strategies in diffractive information processing using linear optical materials by leveraging the precise control that coherent processors offer. Future work could explore the extension of these principles to spatially or temporally incoherent illumination scenarios, further broadening the applicability of diffractive optical processors in practical settings.

Propulsive motion in soft robotic systems requires the power amplification of stored energy. An accumulated strain energy-fracture power-amplification method is used to create light-driven soft robotic systems with a controlled launching ability.

A new technique reveals high-speed trajectories of oscillating vortices and shows that they are 10,000 times lighter than expected.

In many superconductors, applying a sufficiently strong magnetic field causes superconducting electrons to create current vortices that can be drawn along with a steady electric current. To learn more about how these vortices move, researchers have now visualized their trajectories in a situation where they are driven to oscillate at near-terahertz frequencies [1]. They observed the vortex motion on picosecond timescales and found that, under these conditions, a vortex’s effective mass is 10,000 times less than expected. This result may be important for efforts to improve high-current superconducting devices.

Superconductors can only deliver current up to a certain maximum value before the superconductivity is destroyed, which is an important concern for the development of high-current devices. Many researchers consider vortices—which can form even in the absence of an external magnetic field—to be the source of this so-called current-induced quenching. But determining how vortices cause quenching requires better knowledge of vortices and thus better measurements of their motion.

UCLA researchers have conducted an in-depth analysis of nonlinear information encoding strategies for diffractive optical processors, offering new insights into their performance and utility. Their study, published in Light: Science & Applications, compared simpler-to-implement nonlinear encoding strategies that involve phase encoding with the performance of data repetition-based nonlinear information encoding methods, shedding light on their advantages and limitations in the optical processing of visual information.

A supercrystal formation previously unobserved in a metal-insulating material was discovered by a Cornell-led research team, potentially unlocking new ways to engineer materials and devices with tunable electronic properties.

Researchers have made significant advancements in understanding atomic-scale heat motion in materials, crucial for developing solid-state cooling technology.

This technology, which operates without traditional refrigerants or moving parts, uses materials like nickel-cobalt-manganese-indium magnetic shape-memory alloys to exploit the magnetocaloric effect for efficient cooling.

A crucial knowledge gap in atomic-scale heat motion was recently bridged by a research team led by the Department of Energy’s Oak Ridge National Laboratory. This new understanding holds promise for enhancing materials to advance an emerging technology called solid-state cooling.

Researchers have developed methods to explore and utilize superconductivity in non-equilibrium states, such as those induced by laser pulses, at temperatures much higher than traditional superconductors operate.

This light-induced superconductivity has been shown to replicate crucial features like zero electrical resistance and expulsion of magnetic fields, suggesting potential applications in high-speed devices and extending superconductivity to ambient temperatures.

Superconductivity is a remarkable phenomenon that enables a material to carry an electrical current with zero loss. This collective quantum behavior is unique to certain conductors and only occurs at temperatures significantly below room level.

The robotics startup provides a modular cell-based matrix structure through which an AI-powered bot navigates to move materials.

The Department of Energy’s Oak Ridge National Laboratory has publicly released a new set of additive manufacturing data that industry and researchers can use to evaluate and improve the quality of 3D-printed components. The breadth of the datasets can significantly boost efforts to verify the quality of additively manufactured parts using only information gathered during printing, without requiring expensive and time-consuming post-production analysis.

Data has been routinely captured over a decade at DOE’s Manufacturing Demonstration Facility, or MDF, at ORNL, where early-stage research in advanced manufacturing coupled with comprehensive analysis of the resulting components has created a vast trove of information about how 3D printers perform. Years of experience pushing the boundaries of 3D printing with novel materials, machines and controls have provided ORNL with the unique ability to develop and share comprehensive datasets. The newest dataset is now available for free through an online platform.

The conventional manufacturing industry benefits from centuries of quality-control experience. However, additive manufacturing is a newer, non-traditional approach that typically relies on expensive evaluation techniques for monitoring the quality of parts. These techniques might include destructive mechanical testing or non-destructive X-ray computed tomography, which creates detailed cross-sectional images of objects without damaging them.