The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/fa5c829d-0304-4c55-97d5-be1cafa318f9

Puts, Lukas; Lenstra, Daan; Williams, Kevin; Yao, Weiming (2024): Data from modeling published in the paper "Phase-space analysis of a two-section InP laser as an all-optical spiking neuron: dependency on control and design parameters". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/fa5c829d-0304-4c55-97d5-be1cafa318f9.v1

Dataset

• Electrical and Electronic Engineering
• Engineering

dynamical analysis, neuromorphic photonics, optical neuron, optical spiking neuron, rate equation model

CC BY-NC-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Lukas Puts , Daan Lenstra , Kevin Williams , Weiming Yao

This upload contains the complete Python code to generate the figures as shown in the paper "Phase-space analysis of a two-section InP laser as an all-optical spiking neuron: dependency on control and design parameters". The code comprises the Yamada rate equation model to calculate the carrier densities and photon number, as well as underlying compact models to simulate an integrated two-section laser neuron in the generic InP technology platform as mentioned in the paper. To calculate the outcome of the model, Pythons ODE solver is used. A 2D sweep function is implemented to generate the data for the 2D parameter sweep plot (figure 7). Alternatively, two addition folders with pre-generated data using this model is provided to generate this figure. All generated data is saved as figures directly, or in the numpy format.

• 2024-07-10 first online, published, posted

4TU.ResearchData

py, npy, png

Phase-space analysis of a two-section InP laser as an all-optical spiking neuron: dependency on control and design parameters

Eindhoven University of Technology, Photonic Integration Group