Guo Junxiong, Liu Yu, Lin Lin, Li Shangdong, Cai Ji, Chen Jianbo, Huang Wen, Lin Yuan, Xu Jun
Institute of Advanced Study, School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, P. R. China.
School of Integrated Circuits, Tsinghua University, Beijing 100084, P. R. China.
Nano Lett. 2023 Oct 25;23(20):9651-9656. doi: 10.1021/acs.nanolett.3c02194. Epub 2023 Aug 7.
Emerging memory devices have been demonstrated as artificial synapses for neural networks. However, the process of rewriting these synapses is often inefficient, in terms of hardware and energy usage. Herein, we present a novel surface plasmon resonance polarizer-based all-optical synapse for realizing convolutional filters and optical convolutional neural networks. The synaptic device comprises nanoscale crossed gold arrays with varying vertical and horizontal arms that respond strongly to the incident light's polarization angle. The presented synapse in an optical convolutional neural network achieved excellent performance in four different convolutional results for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digit data set. After training on 1,000 images, the network achieved a classification accuracy of over 98% when tested on a separate set of 10,000 images. This presents a promising approach for designing artificial neural networks with efficient hardware and energy consumption, low cost, and scalable fabrication.
新兴的存储设备已被证明可作为神经网络的人工突触。然而，就硬件和能源使用而言，重写这些突触的过程通常效率低下。在此，我们提出一种基于表面等离子体共振偏振器的新型全光突触，用于实现卷积滤波器和光学卷积神经网络。该突触器件由纳米级交叉金阵列组成，其垂直和水平臂各不相同，对入射光的偏振角有强烈响应。所展示的光学卷积神经网络中的突触在对修改后的国家标准与技术研究所（MNIST）手写数字数据集进行分类的四种不同卷积结果中表现出色。在对1000张图像进行训练后，该网络在另一组10000张图像上进行测试时，分类准确率超过98%。这为设计具有高效硬件和低能耗、低成本以及可扩展制造的人工神经网络提供了一种很有前景的方法。
