Mode decomposition is a quantitative technique for analyzing multimode fibers. With pre-knowledge
of the eigenmodes, the phase and amplitude weights of each mode can be extracted from the optical
field. In this paper, we introduce a simple deep learning-based mode decomposition method by
integrating a physical model with a deep neural network. We demonstrate that this method can
decompose up to thousands of modes based on pure-intensity images.
Analysing the optical field of multimode fibers by intensity images machine learning for design in photonics-based sensing and imaging applications. However, existing mathematical algorithms, iterative methods, or AI algorithms encounter scalability issues. In this study, we incorporate the physics principle of mode superposition into neural networks for mode decomposition. This integration eliminates the need for an extensive amount of training data and the time-consuming training process. The proposed method, without pre-training, can effectively perform mode decomposition for up to 220 modes. With the extracted amplitude and phase information, the correlation coefficient between the reconstructed optical field and the original image surpasses 98%. Investigations with noisy data demonstrate the network's efficiency in extracting both phase and magnitude information, even when the signal-to-noise ratio of the image is as low as 1dB which is crucial for secure data communication with multimode fibers.
We experimentally demonstrate a single-frequency Yb-doped multimode fiber amplifier (~76 modes) with a focused Gaussian output beam and enhanced stimulated-Brillouin-scattering (SBS) threshold. With few-mode excitation, the SBS threshold output power (57 W) is already an order of magnitude higher than that for a single-mode fiber amplifier of the same length. The output beam, however, is slightly speckled. To achieve high beam quality, we optimize the input wavefront to focus output light to a single diffraction-limited spot and simultaneously achieve much higher SBS threshold (up to 105 W), because forming a tight output focus requires coherent amplification of many modes.
Modal crosstalk is an issue limiting the deployment of multimode fibers (MMF) in the field of communications. Wavefront shaping techniques can compensate for the scrambling. However, the required coherent measurements usually need a complex optical system. In this paper, we introduce a deep learning-based reference-less method to undo the distortion and perform information transmission through MMF. A deep neural network trained with synthetic data is able to experimentally detect both amplitude and phase information of the light field. By using a spatial light modulator, a desired light field distribution is obtained at the output of MMF.
Adaptive optics and wavefront control enable advances in the transmission matrix measurement. However, despite its rich information content, the transmission matrix is usually only used to enhance light transmission through scattering media. For the first time, we present an assessment of the health status of retinal tissues using the transmission matrix measured by digital holography. Our data show that transmission matrix analysis can detect pathological changes in the retina and is promising for the development of label-free imaging biomarkers.
The retina is an epithelium composed of different cell layers with unique optical properties and detects light by photoreceptor neurons for visual function. The quest for suitable measurement methods to detect the health status of retinal tissues is ongoing. We study the capability of the optical transmission matrix, which fully describes the transition of a light field propagating through a scattering sample. Despite its rich information content, the transmission matrix is commonly just used for light delivery through scattering media. Digital holography is employed to measure the transmitted light. We demonstrate that singular value decomposition of the transmission matrix allows to discriminate phantom tissues with varying scattering coefficient. We apply these findings to retinal organoid tissues. Application of an inducer of retinal damage in animals, caused cell death and structural changes in human retinal organoids, which resulted in distinct changes in the transmission matrix. Our data indicate that the analysis of the transmis-sion matrix can distinguish pathologic changes of the retina towards the development of imag-ing-based biomarkers. Laser microscopy of retinal organoid samples from human induced plu-ripotent stem cells is a disruptive technology that promises paradigm shifts for biomedicine.
KEYWORDS: Data transmission, Multimode fibers, Holography, Space division multiplexing, Quantum efficiency, Transmitters, Time metrology, Spatial light modulators, Signal to noise ratio, Receivers
Holographic transmission matrix measurements of optical multimode fibre communication channels enable optical precoding on the transmitter-side exploiting the physical layer. This approach can achieve a crucial SNR advantage for legitimate communication participants over illegitamte ones, such as an eavesdropper. We present measurements on a 55-mode fibre channel including transmitter Alice and receiver Bob, to which an eavesdropper Eve is physically coupled. Alice performs a programmable optical precoding through an SLM based on singular value decomposition enabling space division multiplexing. Demonstration of confidential data transmission is based on measurements from both channels Alice/Bob and Alice/Eve, which are used for determining the achievable amount of securely exchanged data between Alice and Bob, i.e. secrecy rate. Results are shown on step-index fibres of up to 100 m length.
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