The rapid advancement of artificial intelligence, cloud computing, and high-throughput data processing presents significant challenges to traditional electronic computing systems, which face limitations in power consumption, signal delay, and CMOS technology scaling constraints. Optoelectronic intelligent computing chips (OICCs) have emerged as an innovative computing paradigm that utilizes photons for information transmission, enabling parallel processing, ultra-high-speed operations, and energy efficiency. These chips are positioned to serve as fundamental components in next-generation computing platforms, particularly in demanding applications such as optical signal processing, artificial intelligence acceleration, and quantum information science.

Among various architectures, MZI-based photonic computing structures are widely adopted due to their compact footprint, compatibility with silicon photonics platforms, and ability to perform unitary transformations via programmable phase shifts. However, the precise functionality of these chips is often compromised by fabrication-induced parameter variability, accumulated phase errors in complex interferometric paths, and thermal or environmental disturbances. Traditional static calibration or manual tuning is insufficient for maintaining computational precision in dynamic environments. Hence, the introduction of self-configuration algorithms is essential to endow OICCs with adaptive, robust, and scalable capabilities, making them viable for practical deployment in real-world applications.

This review offers a comprehensive survey of recent developments in self-configuration algorithms designed for MZI-based OICCs, covering their architectural foundations, algorithmic strategies, and typical application scenarios.

We first analyze the programmable nature of 2×2 MZI units and the topological configurations used to build higher-dimensional matrix processors. Forward-only propagation meshes, including triangular and rectangular grids (Fig. 2), offer low-latency, linear transformation capabilities. Meanwhile, cyclic mesh topologies such as quadrilateral, hexagonal, and triangular structures (Fig. 3) provide higher functional density and support feedback paths, essential for realizing optical delay lines, resonant structures, and advanced signal processing functions.

To manage the complexity of tuning such large-scale photonic networks, several algorithmic self-configuration paradigms have been introduced.

Online training algorithms facilitate real-time parameter adjustment based on system feedback. A prominent approach is gradient-based optimization (Fig. 5), which determines the partial derivatives of an objective function with respect to tunable parameters like phase shifters. This can be implemented through forward propagation, finite difference methods, or in situ optical backpropagation. Recent studies incorporate adaptive learning strategies such as Adam optimizers and direct-derivative computation to accelerate convergence and enhance tolerance to system noise.

In addition, bio-inspired global optimization techniques, such as Genetic Algorithms (GA) and Bacterial Foraging Optimization (BFO) (Figs. 6 and 7), are employed to explore complex solution landscapes. These methods, by simulating biological evolution or microbial behavior, have demonstrated strong adaptability in solving non-convex optimization problems across varying operating conditions.

Reference-path-assisted techniques (Fig. 8) represent another significant class of online strategies. By introducing an on-chip optical reference path and exploiting Fourier or Kramers-Kronig relations, both amplitude and phase responses of the signal processing core can be recovered and optimized. These approaches circumvent the need for explicit phase measurement and offer robustness against thermal cross-talk and fabrication-induced loss imbalance, making them ideal for stable and accurate chip configuration.

Offline training strategies predefine optimal control parameters based on empirical models or data under ideal conditions. One classic method is backpropagation combined with stochastic gradient descent (Fig. 9), where a neural network’s weights are trained off-chip and then mapped to the MZI phase matrix on-chip. Structural calibration and error compensation approaches (Fig. 10) address physical non-idealities by characterizing and correcting device-level deviations, enhancing functional reliability without additional hardware overhead. Noise-robust designs (Fig. 11) incorporate regularization and quantization strategies to maintain computational fidelity under environmental and circuit-level disturbances. Lastly, heuristic global optimization techniques such as hybrid genetic algorithms or simulated annealing (Fig. 12) enable efficient configuration of high-dimensional photonic arrays.

Self-configuration algorithms are now recognized as the cornerstone technology enabling practical and large-scale deployment of optoelectronic intelligent computing chips. By tightly integrating optimization algorithms with chip-level hardware control, these systems achieve real-time sensing, autonomous decision-making, and adaptive reconfiguration. The result is a class of computing chips that not only perform complex linear and nonlinear operations but also self-optimize to maintain performance across diverse tasks and unpredictable operating environments.

In application, self-configurable OICCs have demonstrated significant potential. In photonic signal processing, they enable all-optical logic gates, channel routing, and wavelength-selective filters with automatic tuning and recalibration. In artificial intelligence acceleration, they support matrix-vector multiplication and inference in optical neural networks with substantially improved energy efficiency and reduced latency compared to conventional GPU or TPU-based platforms. For example, the “Taichi” architecture, leveraging configurable MZI arrays, achieved near-human performance on large-scale classification tasks, demonstrating the feasibility of programmable photonic computing at scale. In quantum information, the high precision afforded by self-configuration allows robust realization of quantum logic gates, entangled photon state control, and reconfigurable quantum networks using photonic circuits.

Despite significant advances, several critical challenges persist. These encompass improving phase-tuning resolution at the hardware level, implementing efficient feedback control mechanisms, and resolving scalability issues in multi-layer or multi-core photonic architectures. Future research directions may encompass the integrated design of algorithms and photonic device physics, incorporation of AI-based meta-learning for autonomous calibration, and utilization of edge-cloud collaborative frameworks for distributed self-configuration.

In conclusion, self-configuration algorithms represent a fundamental enabler for transforming photonic computing from laboratory prototypes into practical systems. Through sustained interdisciplinary innovation, these methodologies will facilitate new possibilities for intelligent, scalable, and energy-efficient computing within the optical domain.