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Creators/Authors contains: "Khurgin, J_B"

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  1. Non-Hermitian systems have recently attracted significant attention in photonics. One of the hallmarks of these systems is the possibility of realizing asymmetric mode-switching and omni-polarizer action through the dynamic encirclement of exceptional points (EPs). Here, we offer a new perspective on the operating principle of these devices, and we theoretically and experimentally show that linear asymmetric mode-switching and omni-polarizer action can be easily realized—with the same performance and limitations—using simple configurations that emulate the physics involved in encircling EPs without the complexity of actual encirclement schemes. The proposed concept of “encirclement emulators” and our theoretical and experimental results may allow better assessment of the limitations, practical potential, and applications of EP encirclements in non-Hermitian photonics. 
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  2. Recently, integrated optics has become a functional platform for implementing machine learning algorithms and, in particular, neural networks. Photonic integrated circuits can straightforwardly perform vector-matrix multiplications with high efficiency and low power consumption by using weighting mechanism through linear optics. However, this cannot be said for the activation function, i.e., “threshold,” which requires either nonlinear optics or an electro-optic module with an appropriate dynamic range. Even though all-optical nonlinear optics is potentially faster, its current integration is challenging and is rather inefficient. Here, we demonstrate an electroabsorption modulator based on an indium tin oxide layer monolithically integrated into silicon photonic waveguides, whose dynamic range is used as a nonlinear activation function of a photonic neuron. The thresholding mechanism is based on a photodiode, which integrates the weighed products, and whose photovoltage drives the electroabsorption modulator. The synapse and neuron circuit is then constructed to execute a 200-node MNIST classification neural network used for benchmarking the nonlinear activation function and compared with an equivalent electronic module. 
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