Thermal Crosstalk Modelling and Compensation Methods for Programmable Photonic Integrated Circuits

• URL: http://arxiv.org/abs/2404.10589v1
• Date: Tue, 19 Mar 2024 21:19:54 GMT
• Title: Thermal Crosstalk Modelling and Compensation Methods for Programmable Photonic Integrated Circuits
• Authors: Isidora Teofilovic, Ali Cem, David Sanchez-Jacome, Daniel Perez-Lopez, Francesco Da Ros,
• Abstract summary: We train and experimentally evaluate three models to predict the effect of thermal crosstalk in different locations of an integrated programmable photonic mesh. We experimentally validate the models through compensation of the crosstalk-induced wavelength shift. We evaluate the generalization capabilities of one of the models by employing it to predict and compensate for the effect of thermal crosstalk for parts of the chip it was not trained on.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Photonic integrated circuits play an important role in the field of optical computing, promising faster and more energy-efficient operations compared to their digital counterparts. This advantage stems from the inherent suitability of optical signals to carry out matrix multiplication. However, even deterministic phenomena such as thermal crosstalk make precise programming of photonic chips a challenging task. Here, we train and experimentally evaluate three models incorporating varying degrees of physics intuition to predict the effect of thermal crosstalk in different locations of an integrated programmable photonic mesh. We quantify the effect of thermal crosstalk by the resonance wavelength shift in the power spectrum of a microring resonator implemented in the chip, achieving modelling errors <0.5 pm. We experimentally validate the models through compensation of the crosstalk-induced wavelength shift. Finally, we evaluate the generalization capabilities of one of the models by employing it to predict and compensate for the effect of thermal crosstalk for parts of the chip it was not trained on, revealing root-mean-square-errors of <2.0 pm.

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