Chip-Based High-Dimensional Optical Neural Network

Autor: Xinyu Wang, Peng Xie, Bohan Chen, Xingcai Zhang
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Nano-Micro Letters, Vol 14, Iss 1, Pp 1-9 (2022)
Druh dokumentu: article
ISSN: 2311-6706
2150-5551
DOI: 10.1007/s40820-022-00957-8
Popis: 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.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje