Joaquin Matres's Projects
Fully Open Source FASOC generators built on top of OpenROAD
The open-source hardware testing framework.
OPICS : An S-parameter based photonic circuit simulator
Research on optical communication systems
This repository has code for a Python program that synthesizes an optical filter
hybrid optical electronic convolutional neural networks
Simulate optical communications systems with Python.
A simple Pandoc template to build documents and ebooks.
A template for creating epub books from markdown using pandoc.
Base Docker image for PETSc and SLEPc
CAD layout and GDS geometry creation utilities for photonic and superconducting circuits
A conda-smithy repository for phidl.
Fed up with relying on expensive proprietary for your waveguide research? philsol might just be the software for you.
Examples for photonic design and simulation problems using various tools and methods.
Photonic circuit network simulator
List of drivers, interfaces and automation code for optimising coupling to photonic chips.
Jupyter Notebooks for EDX course Photonic Integrated Circuits
Controlling scientific instruments used in optics, photonics and electronics labs
Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch
Data and visualizations for the photontorch paper (Scientific Reports)
Base repository for simulation and control of photonic devices
Python libraries for communicating with optical test equipment, as well as a GUI front-end for automating photonics measurements on a custom wafer-scale optical test setup
Library of easy-to-implement photonic-integrated-circuit (PIC) components for quick GDSII mask layout and design.
Photonic and electronic co-simulation system design tools interfaced with open-source design software like GDSFactory and OpenROAD.
lumopt implementation of a polariztion splitter in Silicon
Practical Python Programming (course by @dabeaz)
Neural network predictor of fabrication variations in integrated photonic devices
Prediction of fabrication variations in integrated photonic devices using deep learning