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硅基超结构光散射乘法累加计算芯片

Silicon-Based Metastructure Optical Scattering Multiply-Accumulate Computation Chip.

作者信息

Liu Xu, Zhu Xudong, Wang Chunqing, Cao Yifan, Wang Baihang, Ou Hanwen, Wu Yizheng, Mei Qixun, Zhang Jialong, Cong Zhe, Liu Rentao

机构信息

National Research Center for Optical Sensing/Communications Integrated Networking, Department of Electronics Engineering, Southeast University, Nanjing 210096, China.

State Key Lab of Mathematical Engineering and Advanced Computing, Wuxi 214125, China.

出版信息

Nanomaterials (Basel). 2022 Jun 21;12(13):2136. doi: 10.3390/nano12132136.

Abstract

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.

摘要

光学神经网络(ONN)已成为取代电子神经网络最具前景的解决方案,它具有带宽大、能耗低、并行处理能力强和超高速等优点。基于硅的微纳集成光子平台已证明与互补金属氧化物半导体(CMOS)工艺具有良好的兼容性。因此,在不完全改变现有基于硅的制造技术的情况下,可以在这样的平台上实现性能更好的光电器件或全光器件。为了满足硅光子计算更小尺寸和更高集成度的要求,利用Lumerical软件对四通道粗波分复用器(CWDM)和光散射单元(OSU)的拓扑结构进行了反向设计和优化。由于随机光功率分配比和非相干性,来自CWDM的不同输入信号的强度可以由后续的OSU直接加权和求和,以完成任意乘加(MAC)运算,从而为散射ONN架构提供核心基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a28/9268426/53947cf07bb0/nanomaterials-12-02136-g001.jpg

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