硅光子学
多路复用器
波分复用
光子学
CMOS芯片
光学计算
电子工程
网络拓扑
材料科学
光子集成电路
计算机科学
光学工程
光网络
硅
光电子学
工程类
多路复用
光学
波长
物理
计算机网络
作者
Xu Liu,Xudong Zhu,Chunqing Wang,Yifan Cao,Baihang Wang,Hanwen Ou,Yizheng Wu,Qixun Mei,Jialong Zhang,Zhe Cong,Rentao Liu
出处
期刊:Nanomaterials
[Multidisciplinary Digital Publishing Institute]
日期:2022-06-21
卷期号:12 (13): 2136-2136
被引量:3
摘要
Optical neural networks (ONN) have become the most promising solution to replacing electronic neural networks, which have the advantages of large bandwidth, low energy consumption, strong parallel processing ability, and super high speed. Silicon-based micro-nano integrated photonic platforms have demonstrated good compatibility with complementary metal oxide semiconductor (CMOS) processing. Therefore, without completely changing the existing silicon-based fabrication technology, optoelectronic hybrid devices or all-optical devices of better performance can be achieved on such platforms. To meet the requirements of smaller size and higher integration for silicon photonic computing, the topology of a four-channel coarse wavelength division multiplexer (CWDM) and an optical scattering unit (OSU) are inversely designed and optimized by Lumerical software. Due to the random optical power splitting ratio and incoherency, the intensities of different input signals from CWDM can be weighted and summed directly by the subsequent OSU to accomplish arbitrary multiply–accumulate (MAC) operations, therefore supplying the core foundation for scattering ONN architecture.
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