基于局部方差的可编程光子干涉仪网格校准
Local Variance-Based Calibration of Programmable Photonic Interferometer Meshes
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中文总结 AI 辅助
研究可编程光子干涉仪网格校准问题，提出基于局部方差的自校准方法，利用强度测量和相位扰动确定校准点，在8×8处理器上实验验证，实现哈达玛变换，该方法无需节点隔离等，是实用校准原语。
中文摘要 AI 辅助
可编程光子干涉仪网格可实现可重构线性光学变换，但其性能关键取决于马赫-曾德尔干涉仪和移相器的精确校准。传统方法常需节点隔离等条件，在大型热调谐网格中愈发困难。本文介绍一种基于局部方差的自校准方法，利用仅强度测量。施加可控相位扰动，从测量输出功率方差最小值确定校准点。对马赫-曾德尔干涉仪，方差有特征|sin(theta)|依赖关系；对移相器，平衡干涉产生互补|cos(phi)|方差特征。通过全自动化两阶段程序在8×8氮化硅可编程光子处理器上实验验证该方法，实现了嵌入式4×4哈达玛变换。结果表明局部输出方差是可编程光子网格简单的校准可观测量，该方法兼容离散随机相位集合，无需传统节点隔离和正交训练场，是可扩展自稳定光子处理器实用的校准原语。
英文摘要
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.
