A foundational component of vision processing is edge and feature detection. In the human eye, this is carried out powerfully via retinal ganglion cells which fight to suppress neuronal firing of neighbouring cells - a process termed 'lateral inhibition'.
Such spatially-distributed activity competition leads to strong nonlinear enhancement of key image features such as edges, enabling more complex vision functionality including object recognition & motion detection.
Software convolutional neural networks draw inspiration from this process and also begin with edge-detection, but in software this functionality is slow & the process intrinsically linear (matrix multiplications between the input image & convolutional kernels), with nonlinearity forced in via subsequent computationally-expensive activation functions such as ReLu.
Here, we present a physical system which reproduces the strongly nonlinear lateral inhibition used in the retina. Using spatially-distributed mode-competition in nanoscale random network lasers, we demonstrate cutting-edge feature detection on complex images, and leverage this for a retinomorphic photonic convolutional neural network with strong performance.
Surface lattice resonance (SLR) lasers are promising for optical communication, optical computing, sensing and LiDAR applications. They have previously shown large-area single-mode emission with low threshold as well as tuneable spectral and angular emission using plasmonic nanoparticles embedded in thin film gain media. We demonstrate a novel device architecture with solid-state epitaxial InP gain medium that coupled to a gold nanoparticle array via a thin SiO2 layer. These plasmonic nanoparticles form SLRs that weakly couple to the InP waveguide mode forming a plasmonic-photonic hybrid mode supporting single-mode lasing with low thresholds. We experimentally and theoretically characterise the system. Our devices show no photobleaching. Combining plasmonic SLRs with epitaxial gain media paves the way for large-area on-chip integration of SLR lasers.
We experimentally study the spectral lasing response of on-chip InP network random lasers under illumination of different input image shapes. Deep-learning models have become increasingly omipresent throughout society. However, they are blighted by exponentially soaring energy demands. Physical implementations of neural networks are emerging as an attractive solution for performing machine learning more energy-efficiently than conventional GPU hardware by mimicking the complex structure of biological brains. However, not many platforms which can natively receive unprocessed raw image data as light have so far been demonstrated – a highly-appealing approach which deserves attention. Here, we demonstrate an optical system with spectral response to image input. Specifically, we report on designable solid-state InP network random lasers, based on random graph networks etched into wafer-bonded InP. The networks lase over a broad wavelength range and show a plethora of modes formed by multiple scattering paths. These modes are highly sensitive to illumination patterns due to their unique and highly overlapping spatial distribution.
Time-varying metasurfaces have recently emerged as a new topic of interest for control of light at the nanoscale and exploration of fundamental physics. We demonstrate time diffraction from a time slit in an unstructured metasurface. In a pump-probe experiment, excitation of the Berreman mode of a thin film of Indium-Tin-Oxide over gold leads to strong, efficient all-optical modulation of the film, and to time diffraction of the probe. In comparison to previous works in unstructured epsilon-near-zero films, we obtain a 6 nm frequency shift and a 23 nm broadening using lower intensities and a significantly lower thickness of 40 nm, which demonstrates the minimal footprint of the structure. The deeply subwavelength nature of the sample makes a time-varying interpretation simple and efficient, paving the way for time-dependent architectures for ultrafast optical experiments.
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