mlsanityData to 14 Oct 2026

Thermal Crosstalk Modelling and Compensation Methods for Programmable Photonic Integrated Circuits

arXiv 2404.10589 ↗First posted 19 Mar 2024cs.ET cs.LG physics.opticscited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 45 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

16 of 45 references.

References · 16

  1. 2022The Forward-Forward Algorithm: Some Preliminary Investigationscited by 266
  2. 2016Direct Feedback Alignment Provides Learning in Deep Neural Networkscited by 240
  3. 2021Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systemscited by 128
  4. 2016Neuromorphic Silicon Photonic Networkscited by 68
  5. 2015Towards Biologically Plausible Deep Learningcited by 66
  6. 2018Training of photonic neural networks through in situ backpropagationcited by 45
  7. 2022Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networkscited by 44
  8. 2023Backpropagation-free Training of Deep Physical Neural Networkscited by 42
  9. 2023The physics of optical computingcited by 33
  10. 2023Forward-Forward Training of an Optical Neural Networkcited by 19
  11. 2021Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignmentcited by 17
  12. 2019Design of optical neural networks with component imprecisionscited by 10
4 more
  1. 2020Modeling Silicon-Photonic Neural Networks under Uncertaintiescited by 7
  2. 2021Silicon microring synapses enable photonic deep learning beyond 9-bit precisioncited by 4
  3. 2022Data-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multiplierscited by 3
  4. 2022AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networkscited by 2

Built on by

Not cited yet.