High-precision programming of large-scale ring resonator circuits with minimal pre-calibration
Abstract: Microring resonators (MRRs) are essential components in large-scale photonic integrated circuits (PICs), but programming these circuits with high precision and efficiency remains an unsolved challenge. Conventional methods rely on complex calibration processes that are both time-consuming and often inaccurate, limiting the scalability of PICs. This work introduces an innovative control method called chip-in-the-loop optimization (ChiL) that addresses this challenge by offering high scalability, precision, fast convergence, and robustness. ChiL reduces the calibration complexity for an $N$ devices system from $O(kN)$ to a single-shot measurement, while maintaining a record-high precision over 9-bit in the presence of system imperfections, including fabrication variances, thermal crosstalk, and temperature drift. Using ChiL, we experimentally demonstrate a photonic solver for computing matrix eigenvalues and eigenvectors with errors on the order of $10{-4}$. Additionally, we achieve a photonic neural network (PNN) with accuracy and a confusion matrix identical to those of digital computers. ChiL offers a practical approach for programming large-scale PICs and bridges the gap between analog photonic and digital electronic computing and signal processing in both scale and precision.
- Q. Xu, B. Schmidt, S. Pradhan, and M. Lipson, “Micrometre-scale silicon electro-optic modulator,” \JournalTitleNature 435, 325–327 (2005).
- X. Wu, C. Huang, K. Xu, et al., “Mode-Division Multiplexing for Silicon Photonic Network-on-Chip,” \JournalTitleJournal of Lightwave Technology 35, 3223–3228 (2017).
- A. Rizzo, A. Novick, V. Gopal, et al., “Massively scalable Kerr comb-driven silicon photonic link,” \JournalTitleNature Photonics 17, 781–790 (2023).
- J. Wang, F. Sciarrino, A. Laing, and M. G. Thompson, “Integrated photonic quantum technologies,” \JournalTitleNature Photonics 14, 273–284 (2020).
- E. Pelucchi, G. Fagas, I. Aharonovich, et al., “The potential and global outlook of integrated photonics for quantum technologies,” \JournalTitleNature Reviews Physics 4, 194–208 (2022).
- Y. Shen, N. C. Harris, S. Skirlo, et al., “Deep learning with coherent nanophotonic circuits,” \JournalTitleNature Photonics 11, 441–446 (2017).
- A. N. Tait, T. F. de Lima, E. Zhou, et al., “Neuromorphic photonic networks using silicon photonic weight banks,” \JournalTitleScientific Reports 7, 7430 (2017).
- J. Feldmann, N. Youngblood, M. Karpov, et al., “Parallel convolutional processing using an integrated photonic tensor core,” \JournalTitleNature 589, 52–58 (2021).
- C. Huang, S. Fujisawa, T. F. de Lima, et al., “A silicon photonic–electronic neural network for fibre nonlinearity compensation,” \JournalTitleNature Electronics 4, 837–844 (2021).
- H. Zhou, J. Dong, J. Cheng, et al., “Photonic matrix multiplication lights up photonic accelerator and beyond,” \JournalTitleLight: Science & Applications 11, 30 (2022).
- T. Fu, J. Zhang, R. Sun, et al., “Optical neural networks: progress and challenges,” \JournalTitleLight: Science & Applications 13, 263 (2024).
- J. Sun, E. Timurdogan, A. Yaacobi, et al., “Large-scale nanophotonic phased array,” \JournalTitleNature 493, 195–199 (2013).
- W. Bogaerts, D. Pérez, J. Capmany, et al., “Programmable photonic circuits,” \JournalTitleNature 586, 207–216 (2020).
- D. Pérez-López, A. López, P. DasMahapatra, and J. Capmany, “Multipurpose self-configuration of programmable photonic circuits,” \JournalTitleNature Communications 11, 6359 (2020).
- D. Miller, “Rationale and challenges for optical interconnects to electronic chips,” \JournalTitleProceedings of the IEEE 88, 728–749 (2000).
- A. W. Poon, X. Luo, F. Xu, and H. Chen, “Cascaded Microresonator-Based Matrix Switch for Silicon On-Chip Optical Interconnection,” \JournalTitleProceedings of the IEEE 97, 1216–1238 (2009).
- H. Jayatilleka, H. Shoman, L. Chrostowski, and S. Shekhar, “Photoconductive heaters enable control of large-scale silicon photonic ring resonator circuits,” \JournalTitleOptica 6, 84–91 (2019).
- J. Capmany, B. Ortega, and D. Pastor, “A tutorial on microwave photonic filters,” \JournalTitleJournal of Lightwave Technology 24, 201–229 (2006).
- H.-C. Liu and A. Yariv, “Synthesis of high-order bandpass filters based on coupled-resonator optical waveguides (CROWs),” \JournalTitleOptics Express 19, 17653–17668 (2011).
- X. Xu, M. Tan, J. Wu, et al., “High performance RF filters via bandwidth scaling with Kerr micro-combs,” \JournalTitleAPL Photonics 4, 026102 (2019).
- L. M. Cohen, K. Wu, K. V. Myilswamy, et al., “Silicon photonic microresonator-based high-resolution line-by-line pulse shaping,” \JournalTitleNature Communications 15, 7878 (2024).
- W. Zhang, J. C. Lederman, T. Ferreira de Lima, et al., “A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency,” \JournalTitleLight: Science & Applications 13, 14 (2024).
- W. Bogaerts, P. De Heyn, T. Van Vaerenbergh, et al., “Silicon microring resonators,” \JournalTitleLaser & Photonics Reviews 6, 47–73 (2012).
- L. Chrostowski, X. Wang, J. Flueckiger, et al., “Impact of Fabrication Non-Uniformity on Chip-Scale Silicon Photonic Integrated Circuits,” in Optical Fiber Communication Conference, (2014), p. Th2A.37.
- W. Zhang, C. Huang, H.-T. Peng, et al., “Silicon microring synapses enable photonic deep learning beyond 9-bit precision,” \JournalTitleOptica 9, 579–584 (2022).
- C. Huang, S. Bilodeau, T. Ferreira de Lima, et al., “Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits,” \JournalTitleAPL Photonics 5, 040803 (2020).
- J. Cheng, Z. He, Y. Guo, et al., “Self-calibrating microring synapse with dual-wavelength synchronization,” \JournalTitlePhotonics Research 11, 347–356 (2023).
- X. Liu, W. Zhang, J. Cheng, et al., “Single-Monitor Calibration for Multiple Microring Synapses,” \JournalTitleACS Photonics 11, 2570–2577 (2024).
- B. Bai, Q. Yang, H. Shu, et al., “Microcomb-based integrated photonic processing unit,” \JournalTitleNature Communications 14, 66 (2023).
- J. Shlens, “A Tutorial on Principal Component Analysis,” \JournalTitlearXiv preprint arXiv:1404.1100 (2014).
- B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with PCA and ICA,” \JournalTitleComputer Vision and Image Understanding 91, 115–137 (2003).
- J. Gary and R. Helgason, “A matrix method for ordinary differential eigenvalue problems,” \JournalTitleJournal of Computational Physics 5, 169–187 (1970).
- K. Liao, C. Li, T. Dai, et al., “Matrix eigenvalue solver based on reconfigurable photonic neural network,” \JournalTitleNanophotonics 11, 4089–4099 (2022).
- A. N. Tait, M. A. Nahmias, B. J. Shastri, and P. R. Prucnal, “Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing,” \JournalTitleJournal of Lightwave Technology 32, 3427–3439 (2014).
- L. G. Wright, T. Onodera, M. M. Stein, et al., “Deep physical neural networks trained with backpropagation,” \JournalTitleNature 601, 549–555 (2022).
- S. Bandyopadhyay, A. Sludds, S. Krastanov, et al., “Single chip photonic deep neural network with accelerated training,” \JournalTitlearXiv preprint arXiv:2208.01623 (2022).
- Z. Zheng, Z. Duan, H. Chen, et al., “Dual adaptive training of photonic neural networks,” \JournalTitleNature Machine Intelligence 5, 1119–1129 (2023).
- Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” \JournalTitleNature 521, 436–444 (2015).
- A. Mehonic and A. J. Kenyon, “Brain-inspired computing needs a master plan,” \JournalTitleNature 604, 255–260 (2022).
- T. Xu, W. Zhang, J. Zhang, et al., “Control-free and efficient integrated photonic neural networks via hardware-aware training and pruning,” \JournalTitleOptica 11, 1039–1049 (2024).
- C. Wu, X. Yang, H. Yu, et al., “Harnessing optoelectronic noises in a photonic generative network,” \JournalTitleScience Advances 8, eabm2956 (2022).
- A. N. Tait, H. Jayatilleka, T. F. D. Lima, et al., “Feedback control for microring weight banks,” \JournalTitleOptics Express 26, 26422–26443 (2018).
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.