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Ti$_3$C$_2$T$_x$ MXene Enabled All-Optical Nonlinear Activation Function for On-Chip Photonic Deep Neural Networks
arXiv - CS - Emerging Technologies Pub Date : 2021-09-19 , DOI: arxiv-2109.09177
Adir Hazan, Barak Ratzker, Danzhen Zhang, Aviad Katiyi, Nachum Frage, Maxim Sokol, Yury Gogotsi, Alina Karabchevsky

Neural networks are one of the first major milestones in developing artificial intelligence systems. The utilisation of integrated photonics in neural networks offers a promising alternative approach to microelectronic and hybrid optical-electronic implementations due to improvements in computational speed and low energy consumption in machine-learning tasks. However, at present, most of the neural network hardware systems are still electronic-based due to a lack of optical realisation of the nonlinear activation function. Here, we experimentally demonstrate two novel approaches for implementing an all-optical neural nonlinear activation function based on utilising unique light-matter interactions in 2D Ti$_3$C$_2$T$_x$ (MXene) in the infrared (IR) range in two configurations: 1) a saturable absorber made of MXene thin film, and 2) a silicon waveguide with MXene flakes overlayer. These configurations may serve as nonlinear units in photonic neural networks, while their nonlinear transfer function can be flexibly designed to optimise the performance of different neuromorphic tasks, depending on the operating wavelength. The proposed configurations are reconfigurable and can therefore be adjusted for various applications without the need to modify the physical structure. We confirm the capability and feasibility of the obtained results in machine-learning applications via an Modified National Institute of Standards and Technology (MNIST) handwritten digit classifications task, with near 99% accuracy. Our developed concept for an all-optical neuron is expected to constitute a major step towards the realization of all-optically implemented deep neural networks.

中文翻译:

Ti$_3$C$_2$T$_x$ MXene 启用全光学非线性激活函数,用于片上光子深度神经网络

神经网络是开发人工智能系统的首要里程碑之一。由于机器学习任务中计算速度的提高和低能耗,神经网络中集成光子学的利用为微电子和混合光电实现提供了一种有前途的替代方法。然而,由于缺乏非线性激活函数的光学实现,目前大多数神经网络硬件系统仍然是基于电子的。在这里,我们通过实验证明了两种新方法,用于实现基于在红外 (IR) 范围内的二维 Ti$_3$C$_2$T$_x$ (MXene) 中独特的光-物质相互作用的全光神经非线性激活函数有两种配置:1)由 MXene 薄膜制成的可饱和吸收器,和 2) 带有 MXene 薄片覆盖层的硅波导。这些配置可以作为光子神经网络中的非线性单元,而它们的非线性传递函数可以根据工作波长灵活设计以优化不同神经形态任务的性能。建议的配置是可重新配置的,因此可以针对各种应用进行调整,而无需修改物理结构。我们通过修改后的国家标准与技术研究所 (MNIST) 手写数字分类任务确认了机器学习应用程序中获得的结果的能力和可行性,准确率接近 99%。我们开发的全光神经元概念有望成为实现全光实现的深度神经网络的重要一步。
更新日期:2021-09-21
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