My work was part of the the European Union project AIMS5.0
The goal was to find an effective and deployable approach for recognizing a particular type of defect, the corrosion , in aluminum wafers in semiconductor production lines
I worked with Keras & Tensorflow frameworks for models implementation and also experienced with some explainability approaches for model validation
The outcome of my work is presented in a publication, which is about to be submitted for review
Formazione
Università degli Studi di Padova
Livello: Computer Engineering - Artificial Intelligence and Robotics
2021 - 2024
Descrizione:
Internship & Master Thesis: A Deep Learning‑based Approach for Semiconductor Manufacturing Images Defect Identification Publication: A Deep Learning Approach for Aluminum Etch Corrosion Recognition for Semiconductor Manufacturing (to be submitted soon to IEEE Transactions on Semiconductor Manufacturing)
Università degli Studi di Padova
Livello: Ingegneria Informatica
2018 - 2021
Descrizione:
Thesis: Application of deep learning on imbalanced datasets for skin lesion classification
Liceo Scientifico Nicolò Tron
Livello: Scienze Applicate
2013 - 2018
Lingue
Inglese
B2 - Intermedio avanzato
Italiano
C2 - Madrelingua
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