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Area Engineering

Machine Learning assisted fitting of VCSEL parameters

keywords LASER A SEMICONDUTTORE, MACHINE LEARNING

Reference persons PAOLO BARDELLA

Description The goal the thesis is to identify a procedure for the automatic extraction of fitting parameters (cavity properties, material characteristics, …) for Vertical Cavity lasers, able to reproduce experimental evidences (on L-I curves, optical spectra, RIN, …) using the VCSEL model implemented in Synopsys Optsim®.
The idea behind this thesis is to use the implemented model (available both in Optsim and as a MATLAB script) performing a large number of simulations varying the parameters and over reasonable ranges and training a Neural Network (NN). Once trained, the NN should be able to return the best combination of parameters able to fit any provided experimental data.
No prerequisites on ML are required and no previous experience with Optsim is needed.


Deadline 26/10/2023      PROPONI LA TUA CANDIDATURA




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