驱动-耗散临界性下的单片集成光子神经网络
Monolithically integrated photonic neural network at driven-dissipative criticality
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology（华中科技大学武汉光电国家研究中心）
- Optics Valley Laboratory（光谷实验室）
- Department of Electronics and Nanoengineering, Aalto University（阿尔托大学电子与纳米工程系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种单片集成有源光子神经网络，利用驱动-耗散临界性作为物理计算资源，在硅光芯片上实现递归计算，以95.2%的准确率完成非线性分类，并在超低信噪比下保持稳健，同时支持时间特征提取和联想恢复。
AI中文摘要：
人工智能日益需要能够进行自适应表征、时间信息处理以及在不确定性下稳健运行的计算架构。然而，传统的光子神经架构通常依赖于预定义的计算操作和分离的功能模块，限制了物理系统利用其内在动力学实现智能的能力。在此，我们展示了一种单片集成的有源光子神经网络（APNN），该网络利用驱动-耗散临界性作为物理计算资源。通过平衡光激发与耗散，系统在临界区域附近运行，在该区域中，微弱的输入相关变化被放大为可分离的表征，同时保持稳定的吸引子动力学。APNN在硅光子芯片上集成了光电非线性阵列和可重构的对称光学耦合矩阵，能够在紧凑的闭环内实现递归物理计算。我们仅使用100个训练样本和44个可训练参数，就实现了95.2%准确率的非线性分类。并且，在-30 dB的超低信噪比下，该网络仍保持稳健。除了静态识别，相同的驱动-耗散动力学还实现了从心电图信号中提取长程时间特征，以及通过吸引子弛豫对损坏模式进行联想恢复。这些结果确立了驱动-耗散临界性作为表征增强、时间信息保持和容错计算的统一机制，为可扩展的光子系统提供了途径，在这些系统中，智能源于内在的物理动力学。
英文摘要：
Artificial intelligence increasingly demands computing architectures capable of adaptive representation, temporal information processing, and robust operation under uncertainty. However, conventional photonic neural architectures typically rely on predefined computational operations and separate functional modules, limiting the ability of physical systems to exploit their intrinsic dynamics for intelligence. Here, we demonstrate a monolithically integrated active photonic neural network (APNN) that harnesses driven-dissipative criticality as a physical computational resource. By balancing optical excitation and dissipation, the system operates near a critical regime where weak input-dependent variations are amplified into separable representations while stable attractor dynamics are preserved. The APNN integrates an optoelectronic nonlinear array and a reconfigurable symmetric optical coupling matrix on a silicon photonic chip, enabling recurrent physical computation within a compact closed loop. We demonstrate nonlinear classification with 95.2% accuracy using only 100 training samples and 44 trainable parameters. And it remains robust at an ultralow signal-to-noise ratio of -30 dB. Beyond static recognition, the same driven-dissipative dynamics enable long-range temporal feature extraction from electrocardiogram signals and associative recovery of corrupted patterns through attractor-based relaxation. These results establish driven-dissipative criticality as a unified mechanism for representation enhancement, temporal information retention, and error-resilient computation, providing a pathway toward scalable photonic systems in which intelligence emerges from intrinsic physical dynamics.
