Non-Reciprocal Ring Resonators

This is the official repository for the paper:
Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing.

Requirements

The following packages are required to run the simulation:

• Lumerical Interconnect
• Matlab
• Python 3.6+
  • The required python packages are listed in requirements.txt file.

Run the simulation

Run the simulation using the following steps:

1. Create lsf file for the simulation using lsf.sh
2. The main lsf runfile: out/lsf/**/*.slurm.lsf
   • using Lumerical Interconnect directly. (not recommended for large simulations)
   • using out/lsf/**/*.lsf.slurm on a cluster.
3. Compile the data
   • using src/compile_data.py -opmc -l out/results/<simulation_results>
   • using src/lsf/*.compile.slurm on a cluster.
4. The results are stored in out/results/ directory.

Plot the results using the following steps:

Note: all the scripts are in src/plot_scripts/ directory.

• simulation_1.py is used to plot the results of simulation 1.
• simulation_2_3.py is used to plot the results of simulation 2 and 3.
• simulation_2_5_to_mat.ipynb converts the results of simulation 2 and 5 to .mat files.
  • figure_1.m and figure_2.m are used to plot the results of simulation 2, 3, 4 and 5.

Extra

• src/plot_scripts/cache_this.py is used to pre cache the results of the simulation for faster plotting.

Device Architecture

Reciprocal Ring Resonator

Non-Reciprocal Ring Resonator

Cite

We would appreciate if you cite the following paper in your publications if you find this code useful:

@article{pintus_integrated_2024,
	title = {Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing},
	issn = {1749-4885, 1749-4893},
	url = {https://www.nature.com/articles/s41566-024-01549-1},
	doi = {10.1038/s41566-024-01549-1},
	abstract = {Abstract
            Processing information in the optical domain promises advantages in both speed and energy efficiency over existing digital hardware for a variety of emerging applications in artificial intelligence and machine learning. A typical approach to photonic processing is to multiply a rapidly changing optical input vector with a matrix of fixed optical weights. However, encoding these weights on-chip using an array of photonic memory cells is currently limited by a wide range of material- and device-level issues, such as the programming speed, extinction ratio and endurance, among others. Here we propose a new approach to encoding optical weights for in-memory photonic computing using magneto-optic memory cells comprising heterogeneously integrated cerium-substituted yttrium iron garnet (Ce:YIG) on silicon micro-ring resonators. We show that leveraging the non-reciprocal phase shift in such magneto-optic materials offers several key advantages over existing architectures, providing a fast (1 ns), efficient (143 fJ per bit) and robust (2.4 billion programming cycles) platform for on-chip optical processing.},
	language = {en},
	urldate = {2024-12-04},
	journal = {Nature Photonics},
	author = {Pintus, Paolo and Dumont, Mario and Shah, Vivswan and Murai, Toshiya and Shoji, Yuya and Huang, Duanni and Moody, Galan and Bowers, John E. and Youngblood, Nathan},
	month = oct,
	year = {2024},
}

Or in textual form:

Pintus, Paolo, Mario Dumont, Vivswan Shah, Toshiya Murai, Yuya Shoji, 
Duanni Huang, Galan Moody, John E. Bowers, and Nathan Youngblood. "Integrated 
non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory 
computing." Nature Photonics (2024): 1-9.

Patent

The device architectures is patented. Please contact the authors for more information.