The exponential growth of artificial intelligence (AI), particularly large-scale neural network models, has led to unprecedented demands on computational power. Traditional electronic computing platforms face significant challenges in meeting these demands due to the breakdown of Moore’s law, the inefficiency of the von Neumann architecture, and the high energy consumption associated with data movement between memory and processing units. These limitations become critical when scaling deep learning models, which increasingly rely on massive parallel matrix computations and nonlinear operations.

Photonic computing, leveraging the intrinsic advantages of light—including high bandwidth, parallelism, and ultra-low latency—has emerged as a compelling alternative. Unlike electronics, photons can propagate without resistive loss and interference, offering superior energy efficiency and speed. Among various photonic computing architectures, microring resonator (MRR)-based systems are particularly promising due to their small footprint, low tuning energy, and compatibility with dense wavelength-division multiplexing (WDM). MRRs not only support scalable and reconfigurable linear operations, such as matrix-vector multiplication, but also exhibit rich nonlinear dynamics arising from Kerr effects, two-photon absorption (TPA), free-carrier effects, and thermo-optic responses. These properties make them well-suited for implementing both the linear weighting and nonlinear activation functions essential in neural network computation. Furthermore, MRRs offer an advantageous platform for building on-chip spiking neurons and all-optical signal processors, which could play a vital role in neuromorphic and event-driven computing paradigms.

As silicon photonics technology matures and large-scale integration becomes feasible, MRR-based systems are expected to provide compact, high-speed, and energy-efficient photonic processors that are well-aligned with the growing requirements of AI workloads in the post-Moore era.

Recent research has demonstrated the feasibility of MRR-based photonic computing systems for both linear and nonlinear tasks. Linear matrix-vector multiplication can be implemented by broadcast-and-weight architecture or crossbar arrays (Figs. 3?4), where input vectors are encoded onto different wavelengths and processed in parallel. In addition, several new computing architectures have been proposed to enhance functionality, including support for optical convolution, bidirectional signal propagation for in-situ training, and high-dimensional tensor computation through mode and frequency multiplexing (Fig. 5).

Nonlinear operations are achieved either all-optically, through the intrinsic nonlinear response of MRRs (Figs. 6?8), or via hybrid optical-electrical-optical (OEO) pathways (Fig. 9). Reconfigurable optical activation functions have been experimentally demonstrated using a range of mechanisms, including MRR-assisted Mach-Zehnder interferometers, thermally tunable phase-change materials, and dynamic modulation of free-carrier density (Fig. 7). These schemes allow for the emulation of activation functions like ReLU, Sigmoid, Softplus, and Radial Basis functions. Additionally, MRR-based photonic neurons have been used to simulate biological spiking neuron behavior, including threshold firing, temporal integration, and refractory periods. These devices achieve nanosecond- to picosecond-scale pulse responses depending on their material system and design (Fig. 8). Multiple OEO photonic neuron designs based on MRRs have been demonstrated, enabling reconfigurable and cascadable nonlinear transfer functions. They have been integrated into end-to-end deep photonic neural networks and have shown strong potential in real-time signal processing (Fig. 9).

Integrated system-level demonstrations include both deep optical neural networks (DONNs) and reservoir computing frameworks. DONNs based on MRRs have exhibited competitive performance in image classification, optical fiber communication signal equalization, and speech recognition tasks, achieving high throughput and low latency in a compact footprint [Figs. 10(a)?(c)]. Meanwhile, MRR-based optical reservoir computing systems, enabled through spatial and temporal multiplexing strategies, have been used to implement time-series tasks such as binary logic, waveform prediction, and speech classification with minimal training overhead [Figs. 10(d)?(f)].

To support high-precision computation, researchers have proposed various calibration and control techniques. These include feedback-based thermal tuning, dual-wavelength monitoring, dithering modulation, and so on, achieving weight tuning precision of over 9 bit (Fig. 11). Additionally, to further enhance network robustness against fabrication variations and environmental drift, novel training techniques such as noise-injection training, optical pruning, and sharpness-aware training have been introduced (Fig. 12).

MRR-based computing has also been demonstrated in a variety of practical applications across different domains (Fig. 13), such as solving differential equations, optical image convolution and classification, and signal equalization and compensation. These demonstrations highlight the versatility and scalability of MRR systems, and show their potential for integration into real-world intelligent processing systems.

MRR-based photonic computing offers a promising path forward for energy-efficient and scalable AI hardware. With the ability to perform both linear and nonlinear computations in compact, low-power photonic circuits, MRRs are well suited for next-generation intelligent systems. Challenges remain, particularly in improving computing precision, robustness against ambient fluctuation and noise, and scaling to large-scale end-to-end networks. However, ongoing research into calibration-free control methods, innovative computing architecture, and advanced integration techniques are steadily addressing these issues. Looking ahead, further innovations in photonic device design, integrated control circuits, and system-level architectures will be crucial for advancing MRR-based computing from lab-scale demonstrations to practical, large-scale deployment.