Deng Zeyu, Dang Zhangqi, Zhang Ziyang
Laboratory of Photonic Integration, School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China.
iScience. 2025 Apr 8;28(5):112376. doi: 10.1016/j.isci.2025.112376. eCollection 2025 May 16.
Compact and efficient photonic integrated circuits (PICs) are promising route to solving modern computing challenges. Traditional PICs using cascaded Mach-Zehnder Interferometers (MZIs) or micro-ring resonators (MRRs) are limited to rigid linear matrix operations, requiring electronics for data compression, nonlinear activation, and post-processing. The dependence on electronic processing counteracts the advantages brought by photonics. Here we propose a photonic chip that tackles this problem. The idea is to apply two sets of electrodes on a multimode waveguide: one set for data loading and the other for shaping the neural network by manipulating the multimode light interference flexibly. The shaping process, following a genetic algorithm, resorts again to optical computation to bypass the gradient acquisition problem. Once trained, the chip handles computation completely in the optical domain. Experimentally 91% classification accuracy is achieved on the Iris dataset. Our approach may bring PICs closer to practical computation applications without electronics overload.
紧凑高效的光子集成电路(PIC)是解决现代计算挑战的一条很有前景的途径。使用级联马赫-曾德尔干涉仪(MZI)或微环谐振器(MRR)的传统PIC仅限于刚性线性矩阵运算,需要电子设备进行数据压缩、非线性激活和后处理。对电子处理的依赖抵消了光子学带来的优势。在此,我们提出了一种解决这一问题的光子芯片。其思路是在多模波导上应用两组电极:一组用于数据加载,另一组用于通过灵活操纵多模光干涉来塑造神经网络。遵循遗传算法的塑造过程再次借助光学计算来绕过梯度获取问题。一旦经过训练,该芯片完全在光域内处理计算。在鸢尾花数据集上通过实验实现了91%的分类准确率。我们的方法可能会使PIC在不过度依赖电子设备的情况下更接近实际计算应用。