With the rapid development of emerging technologies such as big data, the Internet of Things, and artificial intelligence, the performance requirements for computing systems are ever-increasing. Conventional computing hardware, centered on microelectronic technologies, faces fundamental limitations in speed, power consumption, and parallelism, especially when handling complex computational tasks. This has spurred the exploration of novel computing architectures based on new physical mechanisms. Analog optical computing, which leverages the intrinsic physical properties of systems to process information, has emerged as a promising paradigm, offering new solutions by leveraging the advantages of photons, such as high-speed transmission, high bandwidth, and low power consumption. While significant progress has been made in optical neural networks based on coherent waveguide arrays and spatial diffraction, these schemes often utilize only the linear optical response of devices. The lack of tunable nonlinear mechanisms limits their computational power, as real-world information processing tasks are inherently nonlinear. Semiconductor lasers are complex nonlinear dynamical systems as cornerstone of photonics, making them ideal physical platforms for analog optical computing. Their rich dynamics, including periodic oscillations and chaos induced by external perturbations such as optical injection, delayed feedback, and mutual coupling, provide a powerful resource for computation. Moreover, a network of coupled lasers can spontaneously evolve, through physical processes such as mode competition, into a stable minimum-loss state that maps directly to the solution of a specific mathematical optimization problem. Crucially, unlike many optical schemes that only implement linear matrix operations, systems based on semiconductor laser dynamics can realize both linear weighted summation (through injection and coupling) and key nonlinear activation functions (through intrinsic mechanisms like thresholding, gain saturation, and mode competition) within a single device. This allows them to function as complete neural units. Coupled with recent advancements in integrated micro- and nano-photonics, which enable high-density and on-chip integration, the study of semiconductor laser dynamics offers a compelling pathway toward scalable, high-performance, and brain-inspired analog optical computing systems.

This review systematically elaborates on the applications of semiconductor laser dynamics in analog optical computing, focusing on several representative neuromorphic computing paradigms. First, reservoir computing (RC) is discussed, a recurrent neural network framework where only the output layer is trained. We focus on the time-delay architecture, where a single nonlinear node with delayed feedback can emulate a large network of virtual nodes. A semiconductor laser with optical feedback serves as an ideal nonlinear node, performing high-dimensional mapping of input signals [Fig. 3(c)]. The computational performance of such a system critically depends on the delicate balance between consistency and memory capacity, with the optimal operating point often found at the edge of injection locking, where the system retains a rich nonlinear transient response while ensuring reproducibility [Fig. 3(d)]. Recent progress includes the use of vertical cavity surface emitting laser (VCSEL) polarization dynamics to enhance memory capacity, as well as the development of parallel and deep RC architectures on photonic integrated circuits to improve processing capacity and task-specific performance. Second, photonic spiking neural networks (SNNs) are explored, which mimic the behavior of biological neurons. The dynamics of a two-section semiconductor laser with a saturable absorber (SA) can physically emulate the leaky integrate-and-fire (LIF) neuron model [Fig. 4(b)]. Here, the accumulation of carriers in the gain section corresponds to membrane potential integration, while the bleaching of the SA triggers a sharp optical pulse, analogous to a neuron firing. Recent works have extended this concept to replicate a richer set of biologically-plausible neuronal behaviors, including phasic spiking, tonic spiking, and controllable inhibition, by leveraging the complex dynamics of optically-injected VCSELs. Furthermore, other physical mechanisms, such as those in excitable lasers and distributed feedback (DFB) lasers, have been used to demonstrate functionalities like graded-potential signaling and pulse facilitation, laying a solid foundation for more brain-like computing systems. Third, optical Ising machines are reviewed, which solve complex combinatorial optimization problems by finding the ground state of an Ising Hamiltonian [Fig. 5(a)]. The core concept in semiconductor laser-based systems is the use of an injection-locked laser network, where the state of each laser (e.g., polarization) represents an Ising spin. The system spontaneously evolves through mode competition to a global minimum-loss state, which corresponds to the solution of the optimization problem [Figs. 5(b) and (c)]. We highlight the latest advancements toward scalable, all-optical systems using VCSEL arrays coupled with programmable spatial light modulators (SLMs). This approach aims to eliminate electronic bottlenecks by enabling fully programmable coupling matrices, with recent work demonstrating its feasibility through simulations and proof-of-concept experiments, promising an on-chip path toward large-scale optical spin systems. Finally, optical reinforcement learning (RL) is introduced, which tackles decision-making in uncertain environments. We detail the development path from early concepts using laser chaos as a high-speed physical random number generator to more sophisticated schemes that directly control the laser’s internal dynamics. The state-of-the-art is exemplified by the use of chaotic itinerancy in a multimode semiconductor laser [Fig. 6(b)]. In this scheme, different longitudinal modes of the laser correspond to different actions, the natural chaotic hopping between modes provides an intrinsic “exploration” mechanism, and selective optical injection is used to reinforce successful actions, corresponding to the “exploitation” phase [Fig. 6(c)]. This elegant mapping of the exploration-exploitation dilemma onto a physical process has demonstrated superior scalability compared to traditional algorithms.

The research surveyed in this paper demonstrates that the nonlinear dynamics of semiconductor lasers provide a versatile and powerful physical basis for a variety of brain-inspired analog computing paradigms. However, significant challenges remain on the path to practical application. Current systems are often limited in scale and rely on discrete, fiber-coupled components, which are constrained by coupling efficiency and the inherent speed limitations of semiconductor carrier lifetimes. Scalability is a major bottleneck, as the number of nodes increases, the parameter space of the coupling network expands dramatically, making global control complex and fragile. These challenges highlight a fundamental conflict between systems based on discrete components and the technological trend toward high-density photonic integration. Looking forward, the rapid advancements in micro- and nano-lasers offer a promising path to overcoming these limitations. Micro- and nano-scale devices provide significant advantages in terms of reduced size, enhanced coupling efficiency, and faster dynamic response due to effects like Purcell enhancement. The synergy of theoretical modeling and algorithm design must be deepened to guide the structural design of these complex networks. Furthermore, exploiting multidimensional multiplexing of the optical field and leveraging novel physical mechanisms unlocked by non-Hermitian and topological photonics will be crucial. The precise control afforded by these new physical frameworks may lead to novel functionalities and more robust computational systems. In conclusion, the convergence of semiconductor laser dynamics, micro- and nano-photonics, and artificial intelligence algorithms represents a vibrant and promising field of research, poised to contribute significantly to the development of next-generation intelligent analog optical computing systems.