Artificial intelligence is extensively integrated into daily life, technology, and scientific research. Communication and sensing technologies continue to advance rapidly, with high-performance computing emerging as a fundamental component of information technology. In the “post-Moore era” of integrated circuits, research into revolutionary computing systems has become crucial for overcoming computational capability limitations.

Analog photonic computing represents a promising pathway for next-generation revolutionary computing systems, garnering significant attention for three primary reasons. First, the lightwave carrier frequency extends to hundreds of terahertz (THz), where electrical broadband signals appear as narrowband signals under the carrier frequency. Photonic computing systems can accommodate signal bandwidths spanning tens to hundreds of nanometers, enabling broadband high-speed signal processing and computation. Second, photonic computation implements computational mathematical models through equivalent photonic physical structures, with computation latency limited only by delay line requirements, facilitating extremely low-latency computation. Third, photonic systems can simultaneously utilize multiple degrees of freedom, including wavelength, mode, and polarization, for parallel operations, enhancing computation throughput, reducing single-computation power consumption, and achieving superior theoretical energy efficiency.

generalization and specialization. While these approaches share theoretical foundations, they differ substantially in architectural design and engineering implementation. Consequently, they address distinct application scenarios and face different performance requirements and technical challenges. To advance the analog domain and address modern optical systems’ demands for structural compactness and functional integration, a comprehensive review of existing analog photonic computing methods is essential. This analysis of current limitations and future directions aims to provide valuable insights for subsequent research in analog photonic computing.

The fundamental theories and mechanisms of analog photonic computing are now well-established, with clear trajectories toward generalization and specialization. We examine these developmental trends, introducing the basic principles of analog photonic computing. Figure 2 illustrates typical implementation schemes across different dimensions. Table 1 presents a comparative analysis of analog photonic computing, digital electronic computing, and analog electronic computing characteristics, elucidating the theoretical basis for these developmental trends. We then explore generalized and specialized analog photonic computing approaches. Generalized photonic computing develops programmable computing cores, including photonic matrix operations and convolutional operations, through photonic circuits. These cores, supported by memory and control circuits, are dynamically configured for fundamental operations in artificial neural networks, image processing, and optimization problems. This approach involves repeated reprogramming of the photonic computing core to execute complex algorithmic models, ensuring versatility. As illustrated in Fig. 3, research areas encompass matrix computation, tensor convolution, programmable signal processors, brain-inspired computing, nonlinear computation, and computational precision enhancement. Specialized photonic computing focuses on algorithm models for specific applications, developing dedicated analog-domain photonic hardware architectures to optimize computational efficiency for targeted algorithms, similar to electrical application-specific processors. This hardware typically generates real-time results without frequent reconfiguration, offering significant low-latency advantages while limiting algorithmic model complexity and generality. As shown in Fig. 10, research areas include visual perception applications, optical communication systems, microwave radio frequency applications, combinatorial optimization problem solving, and online training methods. Both technological trajectories exhibit distinct characteristics and leverage photonics’ inherent advantages—broadband capability, low latency, and low power consumption—positioning them as viable candidates for next-generation high-performance computing systems, despite ongoing technical challenges.

These inherent physical properties of photonics provide photonic devices with significant potential for high-speed, low-power, and low-latency computing capabilities. As theories and implementation methods for analog photonic computing have matured in recent years, the field has progressed into a development phase primarily focused on engineering and application research, evolving along generalized and specialized trajectories. While these trajectories share similar underlying mechanisms, they diverge in system architectures, modulation methods, performance evaluation metrics, and key technical challenges. We examine technological advancements and challenges from both generalization and specialization perspectives. Generalized analog photonic computing has established comprehensive methodologies for matrix multiplication, convolution, and related operations. Experimental results have validated the performance advantages of photonic computing cores, including superior computing speed and energy efficiency, while various technical approaches have been proposed for high-precision weight modulation, establishing foundations for large-scale engineering implementation. Future breakthroughs in engineering challenges, including integration scale, loss and signal-to-noise ratio, total system power consumption, and software systems, could enable generalized analog photonic computing to provide efficient, high-speed computing clusters for artificial intelligence, big data, and financial applications. Concurrently, specialized analog photonic computing has achieved significant advances in visual perception, optical communication, microwave radio frequency, and optimization problem solving, demonstrating exceptional low-latency performance in specialized scenarios. The advancement of related online training theories and methods suggests potential applications in various edge computing contexts. Future development requires addressing core challenges such as system-task compatibility, nonlinear processing, and dynamic range to facilitate rapid implementation in practical systems for intelligent perception, autonomous driving, and 6G wireless communication, thereby contributing to computing capabilities in the post-Moore era.