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Controlling light propagation across scattering media by wavefront shaping holds great promise for a wide range of communications and imaging applications. But, finding the right shape for the wavefront is a challenge when the mapping between input and output scattered wavefronts (that is, the transmission matrix) is not known. Correlations in transmission matrices, especially the so-called memory effect, have been exploited to address this limitation. However, the traditional memory effect applies to thin scattering layers at a distance from the target, which precludes its use within thick scattering media, such as fog and biological tissue. Here, we theoretically predict and experimentally verify new transmission matrix correlations within thick anisotropically scattering media, with important implications for biomedical imaging and adaptive optics. Light propagating through a scattering medium exhibits correlations in the transmission matrix. A theoretical and experimental study uncovers intensity correlations that survive multiple scattering, which could be exploited for imaging.

We describe an encrypted communication principle that forms a secure link between two parties without electronically saving either of their keys. Instead, random cryptographic bits are kept safe within the unique mesoscopic randomness of two volumetric scattering materials. We demonstrate how a shared set of patterned optical probes can generate 10 gigabits of statistically verified randomness between a pair of unique 2 mm 3 scattering objects. This shared randomness is used to facilitate information-theoretically secure communication following a modified one-time pad protocol. Benefits of volumetric physical storage over electronic memory include the inability to probe, duplicate or selectively reset any bits without fundamentally altering the entire key space. Our ability to securely couple the randomness contained within two unique physical objects can extend to strengthen hardware required by a variety of cryptographic protocols, which is currently a critically weak link in the security pipeline of our increasingly mobile communication culture.

Wavefront shaping (WFS) is a technique for directing light through turbid media. The theoretical aspects of WFS are well understood, and under near-ideal experimental conditions, accurate predictions for the expected signal enhancement can be given. In practice, however, there are many experimental factors that negatively affect the outcome of the experiment. Here, we present a comprehensive overview of these experimental factors, including the effect of sample scattering properties, noise, and response of the spatial light modulator. We present simple means to identify experimental imperfections and to minimize their negative effect on the outcome of the experiment. This paper is accompanied by Python code for automatically quantifying experimental problems using the OpenWFS framework for running and simulating WFS experiments.

Wavefront shaping (WFS) is a technique for controlling the propagation of light. With applications ranging from microscopy to free-space telecommunication, this research field is expanding rapidly. As the field advances, it stands out that many breakthroughs are driven by the development of better software that incorporates increasingly advanced physical models and algorithms. Typical WFS software involves a complex combination of low-level hardware control, signal processing, calibration, troubleshooting, simulation, and the WFS algorithm itself. This complexity makes it hard to compare different algorithms and to extend existing software with new hardware or algorithms. Moreover, the complexity of the software can be a significant barrier for end users of microscopes to adopt WFS. OpenWFS addresses these challenges by providing a modular Python library that separates hardware control from the WFS algorithm itself. Using these elements, a WFS algorithm can be written in a minimal amount of code, with OpenWFS taking care of low-level hardware control, synchronization, and troubleshooting. Algorithms can be used on different hardware or in a completely simulated environment without changing the code. Moreover, we provide full integration with the Micro-Manager microscope control software, enabling WFS experiments to be executed from a user-friendly graphical user interface.

In this paper, we propose a reconfigurable beam-shaping system to permit energy-efficient non-line-of-sight (NLOS) free-space optical communication. Light is steered around obstacles blocking the direct communication pathway and reaches a receiver after reflecting off of a diffuse surface. A coherent array optical transmitter (CAO-Tx) is used to spatially shape the wavefront of the light incident on a diffuse surface. Wavefront shaping is used to enhance the amount of diffusely reflected light reaching the optical receiver. Synthetic NLOS experiments for a signal reflected over an angular range of 20° are presented. A record-breaking 30-Gbit/s orthogonal frequency-division multiplexing signal is transmitted over a diffused optical wireless link with a >17-dB gain.

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow us to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working in this field, which has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.

The modified Born series (MBS) is a fast and accurate method for simulating wave propagation in complex structures. In the current implementation of the MBS, the simulation size is limited by the working memory of a single computer or graphics processing unit (GPU). Here, we present a domain decomposition method that enhances the scalability of the MBS by distributing the computations over multiple GPUs, while maintaining its accuracy, memory efficiency, and guaranteed monotonic convergence. With this new method, the computations can be performed in parallel, and a larger simulation size is possible as it is no longer limited to the memory size of a single computer or GPU. We show how to decompose large problems over subdomains and demonstrate our approach by solving the Helmholtz problem for a complex structure of \\(3.28 10^7\\) cubic wavelengths (\\(320 320 320\\) wavelengths) in just \\(45\\) minutes with a dual-GPU simulation.

The modified Born series method is a fast and accurate method for simulating wave propagation in complex structures. Currently, its main limitation is that the size of the simulation is limited by the working memory of a single computer or graphics processing unit (GPU). Here, we present a domain decomposition method that removes this limitation. We show how to decompose large problems over subdomains while maintaining the accuracy, memory efficiency, and guaranteed monotonic convergence of the method. We demonstrate our approach by solving the Helmholtz problem for a complex structure of size \\(315 315 315\\) wavelengths in just 379 seconds on a dual-GPU system.

We present a method for calibrating the response of a phase-only spatial light modulator in nonlinear microscopy. Our method uses the microscope image itself as calibration measurement and requires no additional hardware components. Our method is adapted to the nonlinear signals encountered in multi-photon excitation fluorescence microscopes, and works well even under low light conditions and with strong photobleaching.