Chip-Based High-Dimensional Optical Neural Network

Xinyu Wang; Peng Xie; Bohan Chen; Xingcai Zhang

doi:10.1007/s40820-022-00957-8

Chip-Based High-Dimensional Optical Neural Network

Nanomicro Lett. 2022 Nov 14;14(1):221. doi: 10.1007/s40820-022-00957-8.

Authors

Xinyu Wang^#¹, Peng Xie^#², Bohan Chen³, Xingcai Zhang^{4

5}

Affiliations

¹ School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100049, China.
² Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK. peng.xie@eng.ox.ac.uk.
³ Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
⁴ School of Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. xingcai@mit.edu.
⁵ John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA. xingcai@mit.edu.

^# Contributed equally.

Abstract

Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems. Optical neural network (ONN) has the native advantages of high parallelization, large bandwidth, and low power consumption to meet the demand of big data. Here, we demonstrate the dual-layer ONN with Mach-Zehnder interferometer (MZI) network and nonlinear layer, while the nonlinear activation function is achieved by optical-electronic signal conversion. Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN. We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution. Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN. This work provides a high-performance architecture for future parallel high-capacity optical analog computing.

Keywords: High-dimension; Integrated optics; Mach–Zehnder interferometer; Nonlinear activation function; Optical neural network; Parallel high-capacity analog computing.