Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Photonics

Fast Phase Calibration of Reconfigurable MZI Optical Processors via BFGS Quasi-Newton Optimization

Donghua Zhou, Quan Luo, Yiyou Fan, Wei Jiang, Jinshan Su

Published Aug 18, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.3390/photonics13080783

Related papers

Robust calibration and energy optimization in reconfigurable photonic processors

Igor A. Litvin, Gökhan Elmas, Kareem El-Safty +2 more

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.

Optics Express•Jun 20, 2025•3 citations

Multiport field-programmable MZI-Based optical processors

Farhad Shokraneh

There has been an escalating demand for dynamically reconfigurable photonic structures to implement any linear optical transformation between the input and output ports. Such devices are often constructed as a mesh of reconfigurable Mach-Zehnder interferometers(MZIs). Consequently, the programmed MZIs can redistribute the light to implementarbitrary unitary transformations from an input vector of optical signals to an outputvector of coherent optical modes. These structures have been widely used as a promising hardware platform for fast and energy-efficient matrix multiplications in different applications, such as information processing, machine learning algorithms, neural networks(NNs), etc. Matrix multiplication is an essential but time- and energy-consuming operation in machine learning computations. The computational complexity of NNs necessitates well designed and implemented hardware accelerators for fast and energy-efficient data processing, which allows for scaling the structure to larger NNs in current and upcoming applications. Over the last few years, the considerable progress in silicon photonics has made it possible to fabricate multiport optical interferometric structures with small ootprints and high-power efficiency, which can be exploited as hardware accelerators, thanks to the inherent parallelism of optics. This Ph.D. thesis presents the theoretical and experimental analysis of multiport reconfigurable MZI-based optical processors. The optical processors are meshes of 2×2 reconfigurable MZIs. It is demonstrated how the optical transformation matrix of the optical processors is theoretically decomposed for a given application. The Reck-mesh-based optical processor is experimentally calibrated before programming the device for a given application. Additionally, the thesis includes the theoretical and measurement results of a single layer NN implemented by a 4×4 Reck-mesh-based optical processor. Finally, a novel phase-error- and loss-tolerant multiport field-programmable MZI-based optical processor for optical NNs is proposed. The new structure with a diamond mesh shape makes use of a set of additional MZIs compared to the commonly used Reck (triangular) mesh. The additional MZIs in the Diamond mesh make its topology more symmetric yielding higher phase error- and loss-tolerance, and also provide extra degrees of freedom in the optimization of ONN weight matrix during training. Furthermore, the better classification performance of the Diamond mesh compared to the Reck mesh is associated with the fact that the Diamond mesh is capable of excluding the light intensity that could have degraded the classification accuracy of the implemented ONNs. This is done by means of the tapered-out wave guides of the additional MZIs in the Diamond mesh, while the optimal light intensity is sent to its outputs for better classification. The demonstration of these analyses is significant as it can be used to implement any universal field-programmable photonic device as an optical processor, i.e., optical computational accelerator, which can be experimentally programmed to implement arbitrary linear optical transformation matrix for a given application. Additionally, the presented analytical results in this thesis can be used to enhance the classification performance of ONNs implemented by such structures. As a result of these analyses, the thesis proposes the Diamond-mesh-based optical processor as a promising candidate for large scale ONNs which outperforms the Reck-mesh-based ones in the presence of experimental imperfections

•Jan 1, 2021•0 citations

Experimental validation of the diagonal optical path properties: mitigating phase errors in interferometric-based optical processors

S. Mohammad Reza Safaee, Hassan Rahbardar Mojaver, Odile Liboiron-Ladouceur

We present an efficient calibration and programming methodology in the presence of imperfections and uncertainties for Mach-Zehnder interferometer (MZI)-based optical processors, utilizing the diagonal optical path properties. This approach enables direct phase monitoring of MZI phase shifters, inherently suppressing calibration errors caused by spurious scattered light originating from non-diagonal blocks and eliminating the need for computationally intensive calibration/programming schemes. We experimentally validate these properties using a 4 × 4 interferometric mesh fabricated on a silicon-on-insulator platform, demonstrating that the calibration remains unaffected by phase-setting uncertainties in preceding or succeeding blocks on a diagonal path. We also present a benchmarking procedure to assess testbed fidelity, which is further used to confirm the effectiveness of our approach by programming two random weight matrices, where fine-tuning via the diagonal path reduces the mean error of matrix-vector multiplication by 79% compared to an offline calibration method. These results highlight diagonal path properties as a practical and scalable solution for calibrating and programming reconfigurable multiport interferometers.

Optics Express•Aug 6, 2025•5 citations

Local Variance-Based Calibration of Programmable Photonic Interferometer Meshes

Gökhan Elmas, Igor A. Litvin, Janis Nötzel

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.

physics.opticsCross-listedarXiv:2607.14759•Jul 17, 2026

Towards Phase-Error- and Loss-Tolerant Programmable MZI-Based Optical Processors for Optical Neural Networks

Farhad Shokraneh, Simon Geoffroy-Gagnon, Odile Liboiron-Ladouceur

Mach Zehnder Interferometer-based reconfigurable structures are promising candidates for fast and power-efficient computations in optical neural networks. This work presents the classification performance analysis of a single layer optical neural network implemented by Reck and Clements meshes in the presence of experimental imperfections.

IEEE Photonics Conference•Sep 1, 2020•6 citations

High-fidelity and compact topology architecture for large-scale reconfigurable linear optical networks

Shuai Lin, Jinjie Zeng, Shuqing Lin +2 more

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.

Advanced Photonics Nexus•Nov 19, 2025•3 citations
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.
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.
There has been an escalating demand for dynamically reconfigurable photonic structures to implement any linear optical transformation between the input and output ports. Such devices are often constructed as a mesh of reconfigurable Mach-Zehnder interferometers(MZIs). Consequently, the programmed MZIs can redistribute the light to implementarbitrary unitary transformations from an input vector of optical signals to an outputvector of coherent optical modes. These structures have been widely used as a promising hardware platform for fast and energy-efficient matrix multiplications in different applications, such as information processing, machine learning algorithms, neural networks(NNs), etc. Matrix multiplication is an essential but time- and energy-consuming operation in machine learning computations. The computational complexity of NNs necessitates well designed and implemented hardware accelerators for fast and energy-efficient data processing, which allows for scaling the structure to larger NNs in current and upcoming applications. Over the last few years, the considerable progress in silicon photonics has made it possible to fabricate multiport optical interferometric structures with small ootprints and high-power efficiency, which can be exploited as hardware accelerators, thanks to the inherent parallelism of optics. This Ph.D. thesis presents the theoretical and experimental analysis of multiport reconfigurable MZI-based optical processors. The optical processors are meshes of 2×2 reconfigurable MZIs. It is demonstrated how the optical transformation matrix of the optical processors is theoretically decomposed for a given application. The Reck-mesh-based optical processor is experimentally calibrated before programming the device for a given application. Additionally, the thesis includes the theoretical and measurement results of a single layer NN implemented by a 4×4 Reck-mesh-based optical processor. Finally, a novel phase-error- and loss-tolerant multiport field-programmable MZI-based optical processor for optical NNs is proposed. The new structure with a diamond mesh shape makes use of a set of additional MZIs compared to the commonly used Reck (triangular) mesh. The additional MZIs in the Diamond mesh make its topology more symmetric yielding higher phase error- and loss-tolerance, and also provide extra degrees of freedom in the optimization of ONN weight matrix during training. Furthermore, the better classification performance of the Diamond mesh compared to the Reck mesh is associated with the fact that the Diamond mesh is capable of excluding the light intensity that could have degraded the classification accuracy of the implemented ONNs. This is done by means of the tapered-out wave guides of the additional MZIs in the Diamond mesh, while the optimal light intensity is sent to its outputs for better classification. The demonstration of these analyses is significant as it can be used to implement any universal field-programmable photonic device as an optical processor, i.e., optical computational accelerator, which can be experimentally programmed to implement arbitrary linear optical transformation matrix for a given application. Additionally, the presented analytical results in this thesis can be used to enhance the classification performance of ONNs implemented by such structures. As a result of these analyses, the thesis proposes the Diamond-mesh-based optical processor as a promising candidate for large scale ONNs which outperforms the Reck-mesh-based ones in the presence of experimental imperfections
We present an efficient calibration and programming methodology in the presence of imperfections and uncertainties for Mach-Zehnder interferometer (MZI)-based optical processors, utilizing the diagonal optical path properties. This approach enables direct phase monitoring of MZI phase shifters, inherently suppressing calibration errors caused by spurious scattered light originating from non-diagonal blocks and eliminating the need for computationally intensive calibration/programming schemes. We experimentally validate these properties using a 4 × 4 interferometric mesh fabricated on a silicon-on-insulator platform, demonstrating that the calibration remains unaffected by phase-setting uncertainties in preceding or succeeding blocks on a diagonal path. We also present a benchmarking procedure to assess testbed fidelity, which is further used to confirm the effectiveness of our approach by programming two random weight matrices, where fine-tuning via the diagonal path reduces the mean error of matrix-vector multiplication by 79% compared to an offline calibration method. These results highlight diagonal path properties as a practical and scalable solution for calibrating and programming reconfigurable multiport interferometers.
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.
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.