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Updated: Apr 30, 2026

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通过芯片上全光学调制和光子神经网络实现的传感器内成像分类

Jingxiang Song, Xin Xin, Nicolas A F Jaeger

|September 23, 2025

概括

本研究引入了使用光子学的传感器内成像分类系统,减少自动驾驶和安全应用中的冗余数据. 新型光子神经网络在MNIST数据集上达到96.82%的准确性.

科学领域:

光学和光学工程的光学和光学工程.
人工智能和机器学习
集成电路设计集成电路设计

背景情况:

自动驾驶和安全中的图像传感器由于传感器与处理器的分离,会产生过多的数据.
目前的系统面临的挑战是,传感器终端和计算单元之间的数据传输是多余的.

研究的目的:

通过整合传感和光子神经网络来开发"传感器内"成像分类解决方案.
克服当前图像传感技术中的数据冗余问题.
展示一种新的光子方法,用于高效的图像处理.

主要方法:

利用光子集成技术将传感器组件与光子神经网络结合起来.
开发了一种微环阵列,将可见光转换为近红外信号,用于波导传播.
采用基于PN-doped微光振器的全光学调制器,利用热光学和等离子散散效应.
实现级联式微环用于信号转换和权重应用,使得点产品操作成为可能.

主要成果:

通过控制热光学和等离子散射效应,实现了15dB的调制深度.
展示了使用级联微环的有效分辨率接近8位的点产品操作.
在修改国家标准与技术研究所 (MNIST) 数据集上验证了传感器内图,识别准确率为96.82%.

结论:

拟议的传感器内成像分类技术显著减少了数据冗余.
光子集成电路为先进的"传感器内"处理提供了一个有前途的平台.
这项技术在自动驾驶,安全和其他需要高效图像分析的领域都有潜在的应用.

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