Reconfigurable photonic processors suffer performance loss from fabrication tolerances, thermal drift, and other phase- and amplitude-error sources. We introduce an energy-aware calibration routine that simultaneously restores accuracy and minimizes heater power. After output-channel normalization, every Mach-Zehnder interferometer and external phase shifter is retuned by fitting measured transfer matrices to theory, halving the error in a 4 × 4 Hadamard-transform test. Three complementary optimizations-a global phase offset, selection of the lower-voltage branch for each SU(2) rotation, and permutation of matrix rows-substantially reduce total electrical power without degrading fidelity. This precision, power-conscious calibration removes a key obstacle to scalable, high-performance photonic computing systems.
Reconfigurable photonic processors suffer performance loss from fabrication tolerances, thermal drift, and other phase- and amplitude-error sources. We introduce an energy-aware calibration routine that simultaneously restores accuracy and minimizes heater power. After output-channel normalization, every Mach-Zehnder interferometer and external phase shifter is retuned by fitting measured transfer matrices to theory, halving the error in a 4 × 4 Hadamard-transform test. Three complementary optimizations-a global phase offset, selection of the lower-voltage branch for each SU(2) rotation, and permutation of matrix rows-substantially reduce total electrical power without degrading fidelity. This precision, power-conscious calibration removes a key obstacle to scalable, high-performance photonic computing systems.
Programmable photonic interferometer meshes enable reconfigurable linear optical transformations, but their performance depends critically on accurate calibration of Mach-Zehnder interferometers and phase shifters. Conventional methods often require node isolation, dedicated routing paths, orthogonal training states, reference channels, or prior phase-voltage characterization, which become increasingly difficult in large thermally tuned meshes.
We introduce a local variance-based self-calibration method using intensity-only measurements. Controlled phase perturbations are applied, and calibration points are identified from minima of the measured output-power variance. For Mach-Zehnder interferometers, the variance follows a characteristic |sin(theta)| dependence, allowing bar and cross operating points to be found without conventional node isolation. For phase shifters, balanced interference produces a complementary |cos(phi)| variance signature, enabling quadrature calibration through the same statistical principle.
We validate the method experimentally on an 8 x 8 silicon nitride programmable photonic processor using a fully automated two-stage procedure. Starting from random phase settings, all Mach-Zehnder interferometers are calibrated first, followed by phase-shifter calibration under balanced-interference conditions. As a system-level test, we implement an embedded 4 x 4 Hadamard transformation on the 8 x 8 processor using a Clements decomposition.
These results establish local output variance as a simple calibration observable for programmable photonic meshes. The method is compatible with discrete random phase ensembles and requires neither conventional node isolation nor orthogonal training fields, making it a practical calibration primitive for scalable self-stabilizing photonic processors.
Manufacturing errors introduce phase deviations in Mach–Zehnder interferometers (MZIs) that degrade the fidelity of optical processors. To address this issue, we employ a Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton method for phase calibration of a 4×4 reconfigurable MZI optical processor based on the Reck architecture. By optimizing the mapping from the target matrix to the optical network, the method determines the optimized phase parameters of 12 phase shifters. Thermo-optic simulations are further used to establish the relationship between the applied bias voltage and the induced phase shift, providing a link between the optimized phase parameters and the electrical driving conditions. Compared with Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Gradient Descent with Momentum (GDM), the BFGS method provides faster convergence and high calibration fidelity. These results demonstrate an efficient approach for phase calibration of programmable MZI optical processors.
Programmable photonic integrated circuits can realize analog matrix multiplication to accelerate computing disruptively in various fields. However, a major challenge is the precise voltage configuration of the circuit to deal with the universal static error derived from manufacturing. Here, we propose a complete off-chip method based on the combination of gradient descent and genetic algorithms to find the optimal configuration for an arbitrary matrix, enabling imperfect circuits to achieve excellent performance. In the simulation, we demonstrated that our method implements an arbitrary matrix with an average fidelity of 0.992 on a Mach–Zehnder-interferometer-based circuit with up to 28 input ports. Experimentally, we demonstrated superior performance on the circuit with 4 input ports, including training a theoretical model that characterized the experimental imperfections of the fabricated chip and obtaining the optimal configuration for permutation matrices with near-one fidelity and for 100 unitary matrices with a 0.985 average fidelity. Programmable photonic integrated circuits (PPICs) promise transformative advances in computing, yet precise voltage configuration remains a challenge due to manufacturing-induced static errors. Here, the authors introduce an off-chip method combining gradient descent and genetic algorithms to optimize circuit configurations, achieving high fidelity in matrix implementations and enhancing PPIC performance across various applications.
Programmable integrated photonics (PIP) has emerged as a powerful on-chip platform for optical signal processing and computing, enabling the implementation of reconfigurable N×N unitary matrix transformations through meshes of tunable interferometers, which realize 2$\times2unitarymatrices.However,theenergyconsumptionassociatedwithphase−shifteractuationisbecomingamajorlimitationtothescalabilityofPIPplatforms.Here,weintroduceageometricframeworkforenergyoptimizationinPIPcircuitsbyexploitingtherepresentationof2\times2unitarymatricesasconcatenationsofbasicEulerrotationsontheBlochsphere.WeshowthatequivalentimplementationsofthesameN\times$N unitary matrix (N ≥ 2) can exhibit markedly different energy costs depending on the rotation trajectories on the Bloch sphere implemented by each interferometer. Leveraging this insight, we identify minimum-energy configurations by systematically selecting the shortest rotation trajectories. We experimentally and numerically validate the proposed approach in diverse silicon PIP architectures, including feedforward and multipurpose hexagonal meshes, neural network accelerators, and photonic quantum-gate implementations. These results establish a general route toward more energy-efficient large-scale PIP processors for classical and quantum signal processing and computing applications.
Photonic processors have emerged as an attractive platform for fast and energy-efficient matrix-vector multiplication. However, they are susceptible to error due to their analog nature. Here, we present an error-correction technique that implements a correction offset to the optical en-/decoders of photonic processors. Our proposed method is general-purpose, does not require introducing any additional components to the photonic network, and can address errors stemming from unbalanced losses, 50/50 beamsplitter deviations, digital-to-analog conversion inaccuracies, and any unknown sources. In particular, we show that our method is highly effective in mitigating unbalanced-loss errors, a problem that has not previously been addressed by any error-correction technique. Using this approach, we achieve over 90% error reduction in large triangular meshes, overcoming a key obstacle to highly accurate photonic processors for information processing.
Reconfigurable linear optical networks based on Mach-Zehnder interferometer (MZI) offer significant potential in optical information processing, particularly in emerging photonic quantum computing systems. However, device losses and calibration errors accumulate as network complexity grows, posing challenges in performing precise mapping of matrix operations. Existing architectures, such as Diamond and Bokun, introduce MZI redundancy into Reck and Clements architectures to improve reliability, which increases complexity and differential path losses that limit scalability. We propose a compact topology architecture that achieves 100% fidelity by employing a symmetrical MZI to decouple optical loss from power ratio and introducing extra MZIs to enforce uniform loss distributions. This multi-level optimization enables direct monitoring pathways while supporting precise calibration, and it approaches theoretical fidelity in practical deployments with direct implications for scalable and fault-tolerant photonic computing systems.