Institute of Experimental Physics, Faculty of Physics, University of Warsaw, ul. Pasteura 5, PL-02-093 Warsaw, Poland.
Institute of Physics, Polish Academy of Sciences, Aleja Lotników 32/46, PL-02-668 Warsaw, Poland.
Nano Lett. 2021 May 12;21(9):3715-3720. doi: 10.1021/acs.nanolett.0c04696. Epub 2021 Feb 26.
The rapid development of artificial neural networks and applied artificial intelligence has led to many applications. However, current software implementation of neural networks is severely limited in terms of performance and energy efficiency. It is believed that further progress requires the development of neuromorphic systems, in which hardware directly mimics the neuronal network structure of a human brain. Here, we propose theoretically and realize experimentally an optical network of nodes performing binary operations. The nonlinearity required for efficient computation is provided by semiconductor microcavities in the strong quantum light-matter coupling regime, which exhibit exciton-polariton interactions. We demonstrate the system performance against a pattern recognition task, obtaining accuracy on a par with state-of-the-art hardware implementations. Our work opens the way to ultrafast and energy-efficient neuromorphic systems taking advantage of ultrastrong optical nonlinearity of polaritons.
人工神经网络和应用人工智能的快速发展带来了许多应用。然而，当前神经网络的软件实现在性能和能效方面受到严重限制。人们认为，进一步的进展需要开发神经形态系统，其中硬件直接模拟人脑的神经元网络结构。在这里，我们从理论上提出并实验实现了一个执行二进制操作的节点的光学网络。高效计算所需的非线性由处于强量子光物质耦合状态的半导体微腔提供，其表现出激子极化激元相互作用。我们针对模式识别任务展示了系统性能，获得了与最先进的硬件实现相当的准确性。我们的工作为利用极化激元的超强光学非线性来实现超快速和高能效的神经形态系统开辟了道路。
