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Bibliographic Details
Main Author: Zhang, Zijian
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2504.05685
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author Zhang, Zijian
author_facet Zhang, Zijian
contents We present an efficient method for evaluating random phase errors in phase shifters within photonic integrated circuits, avoiding the computational cost of traditional Monte Carlo simulations. By modeling spatially correlated manufacturing variations as a stationary Gaussian process and applying linear functional theory, our approach enables rapid prediction of phase difference distributions through numerical integration. We validate the method using tolerance optimization and virtual manufacturing experiments, demonstrating its accuracy and significant reduction in computational burden. This framework offers a practical and scalable alternative for error analysis and robust design in photonic systems.
format Preprint
id arxiv_https___arxiv_org_abs_2504_05685
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Fast Prediction of Phase shifters Phase Error Distributions Using Gaussian Processes
Zhang, Zijian
Optics
We present an efficient method for evaluating random phase errors in phase shifters within photonic integrated circuits, avoiding the computational cost of traditional Monte Carlo simulations. By modeling spatially correlated manufacturing variations as a stationary Gaussian process and applying linear functional theory, our approach enables rapid prediction of phase difference distributions through numerical integration. We validate the method using tolerance optimization and virtual manufacturing experiments, demonstrating its accuracy and significant reduction in computational burden. This framework offers a practical and scalable alternative for error analysis and robust design in photonic systems.
title Fast Prediction of Phase shifters Phase Error Distributions Using Gaussian Processes
topic Optics
url https://arxiv.org/abs/2504.05685