Modeling Silicon-Photonic Neural Networks under Uncertainties

Photonic spiking neural networks and CMOS-compatible graphene-on-silicon spiking neurons

Spiking neural networks are known to be superior over artificial neural networks for their computational power efficiency and noise robustness. The benefits of spiking coupled with the high-bandwidth and low-latency of photonics can enable highly-efficient, noise-robust, high-speed neural processors. The landscape of photonic spiking neurons consists of an overwhelming majority of excitable lasers and a few demonstrations on nonlinear optical cavities. The silicon platform is best poised to host a scalable photonic technology given its CMOS-compatibility and low optical loss. Here, we present …

硅光子学测试：光子集成电路的可测试性设计、故障检测与制造变异分析

Silicon Photonics Testing: Design for Testability, Fault Detection, and Manufacturing Variation Analysis in Photonic Integrated Circuits

提出一种硅光子集成电路的可测试性设计（DFT）方法与架构，通过仿真验证测试通路和故障检测电路，并在光神经网络和光逻辑电路中展示其应用。

Quantifying power use in silicon photonic neural networks

Due to challenging efficiency limits facing conventional and unconventional electronic architectures, information processors based on photonics have attracted renewed interest. Research communities have yet to settle on definitive techniques to describe the performance of this class of information processors. Photonic systems are different from electronic ones, so the existing concepts of computer performance measurement cannot necessarily apply. In this manuscript, we attempt to quantify the power use of photonic neural networks with state-of-the-art and future hardware. We derive scaling law…

Nonlinear response of Silicon Photonics microresonators for reservoir computing neural network

Nowadays, Information Photonics is extensively studied and sees applications in many fields. The interest in this breakthrough technology is mainly stimulated by the possibility of achieving real-time data processing for high-bandwidth applications, still implemented through small-footprint devices that would allow for breaking the limit imposed by Moore's law. One potential breakthrough implementation of information photonics is via integrated photonic circuits. Within this approach, the most suitable computational scheme is achieved by integrated photonic neural networks. In this chapter, we…

ReSiPI: A Reconfigurable Silicon-Photonic 2.5D Chiplet Network with PCMs for Energy-Efficient Interposer Communication

2.5D chiplet systems have been proposed to improve the low manufacturing yield of large-scale chips. However, connecting the chiplets through an electronic interposer imposes a high traffic load on the interposer network. Silicon photonics technology has shown great promise towards handling a high volume of traffic with low latency in intra-chip network-on-chip (NoC) fabrics. Although recent advances in silicon photonic devices have extended photonic NoCs to enable high bandwidth communication in 2.5D chiplet systems, such interposer-based photonic networks still suffer from high power consump…

透明导电氧化物与低损耗富氮化硅波导作为神经形态光子学的构建模块

Transparent conductive oxides and low loss nitride-rich silicon waveguides as building blocks for neuromorphic photonics

提出一种结合低损耗富氮化硅波导与透明导电氧化物的新型光子平台，利用其高非线性与双稳态特性，实现完全兼容CMOS的光子存储器件，以突破电子处理限制并构建超快人工神经网络。