arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2409.08070physics.optics

使用部分相干光的全光学傅里叶神经网络

All-optical Fourier neural network using partially coherent light

Jianwei Qin, Yanbing Liu, Yan Liu, Xun Liu, Wei Li, Fangwei Ye

AI总结:

本研究提出在全光学傅里叶神经网络中利用空间非相干光,实验表明其能缓解衍射环等实验误差,从而出人意料地提升了目标识别准确率,为光学网络优化提供了新方案。

摘要:

光学神经网络相比传统电学网络具有加速数据处理和降低能耗等显著优势。尽管相干光通常用于光学神经网络,但本研究提出在全光学傅里叶神经网络中利用空间非相干光。与数值预测中目标识别准确率随非相干性增加而下降的结论相反,我们的实验结果展示了一个令人意外的结果:非相干光提升了准确率。我们将这种意外的性能提升归因于空间非相干光能够缓解衍射环、激光散斑和边缘效应等实验误差。我们的对照实验通过让单色光经过配备动态变化随机相位阵列的空间光调制器来引入空间非相干性。这些发现凸显了部分相干光在优化光学神经网络方面的潜力,为需要在各种条件下保持一致准确性和鲁棒性的应用提供可靠且高效的解决方案。

英文摘要:

Optical neural networks present distinct advantages over traditional electrical counterparts, such as accelerated data processing and reduced energy consumption. While coherent light is conventionally employed in optical neural networks, our study proposes harnessing spatially incoherent light in all-optical Fourier neural networks. Contrary to numerical predictions of declining target recognition accuracy with increased incoherence, our experimental results demonstrate a surprising outcome: improved accuracy with incoherent light. We attribute this unexpected enhancement to spatially incoherent light's ability to alleviate experimental errors like diffraction rings, laser speckle, and edge effects. Our controlled experiments introduced spatial incoherence by passing monochromatic light through a spatial light modulator featuring a dynamically changing random phase array. These findings underscore partially coherent light's potential to optimize optical neural networks, delivering dependable and efficient solutions for applications demanding consistent accuracy and robustness across diverse conditions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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