Optical chips are emerging as a pivotal enabling technology for Physical AI. Unlike conventional electronic chips that rely on electric currents to process information, optical chips use photons for data transmission and computation, offering ultra-high speed, low power consumption, and massive parallelism—making them exceptionally well-suited for the matrix operations prevalent in AI models, such as neural network inference and attention mechanisms. Recent advances have demonstrated photonic neural networks on silicon photonics platforms, where light’s phase, amplitude, and interference are precisely controlled to emulate synaptic weights and perform AI inference directly in hardware. Crucially, optical chips naturally support analog computing, bypassing the latency and energy overhead of digital conversion and dramatically improving computational efficiency. In the context of Physical AI—a paradigm emphasizing tight integration between intelligent systems and the physical world—optical chips not only accelerate perception (e.g., LiDAR, imaging) and decision-making but can also be embedded into robots, autonomous vehicles, and edge devices to enable real-time, low-latency, and highly reliable intelligent responses. With ongoing progress in materials science, nanofabrication, and photonic integration, optical chips are poised to become a cornerstone of next-generation AI infrastructure, paving the way for efficient, sustainable, and distributed intelligent systems.
光学芯片正成为推动物理人工智能（Physical AI）发展的关键使能技术。与传统电子芯片依赖电流处理信息不同，光学芯片利用光子进行数据传输与计算，具备超高速度、低功耗和高并行性等优势。这些特性使其特别适合执行AI模型中大量矩阵运算——如神经网络中的前向传播和注意力机制。近年来，研究人员已成功在硅基光子平台上实现光神经网络，通过调控光的相位、振幅和干涉来模拟神经元连接，从而在硬件层面直接完成AI推理任务。更重要的是，光学芯片天然支持模拟计算，可绕过数字转换带来的延迟和能耗，显著提升能效比。在物理AI领域，即强调智能系统与物理世界紧密耦合的范式下，光学芯片不仅能加速感知（如激光雷达、成像）和决策过程，还能集成于机器人、自动驾驶和边缘设备中，实现低延迟、高可靠性的实时智能响应。随着材料科学、纳米制造和光子集成技术的进步，光学芯片有望成为下一代AI基础设施的核心组件，为构建高效、绿色、分布式的智能系统提供全新路径。
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