Position 2H University Intern - FR CMOS Device Engineering

About GlobalFoundries

GlobalFoundries(GF)is a leading full-service semiconductor foundry providing a unique combination of design, development, and fabrication services to some of the world's most inspired technology companies. With a global manufacturing footprint spanning three continents,GFmakes possible the technologies and systems that transform industries and give customers the power to shape their markets. For more information, visit.

Internship Program Overview:

Our Internsare ourtalent pipeline for GF across the globe. Our goal is to provide students withmeaningfulwork experience that will equip them with the skills to embark on a career in the fast-paced and growing semiconductor industry after graduation. As an intern at GF,you willexperience one-on-one mentorship, work assignments that prioritize your growth and potential, professional development opportunities, and the chance to network with executives.

Project Title:

Characterization and Design of Electronic Devices Using Machine Learning

Project Objective:

Semiconductor devices like transistors, optical sensors and magnetic sensors are key devices that could be employed for a wide range of applications. In this work, the student will explore the use of deep learning to model and optimize electronic device performance based on design parameters such as architecture, geometry, doping profiles, process conditions and operation conditions. The goal is to enable inverse design by training models on experimental data.

Project Description:

The intern will get exclusive insights and hands-on experience in GlobalFoundries state-of-the-art semiconductor characterization facility. He or she will develop models to enable the optimization of semiconductor devices. Key tasks:

. Perform device testing and data collection across various platforms

. Develop scripts for data processing, analysis, and visualization

. Prepare and clean datasets for machine learning workflows

. Train and validate deep learning models for performance prediction

. Apply inverse design techniques to generate optimal design parameters

. Optimize model accuracy through iterative training, validation, and refinement

. In the course of development, additional hardware design on PCB might be required to enable specific characterization. During the final phase, the student will validate the setup by characterizing the devices

Project Outcome:

The intern will gain hands-on experience in device characterization, data engineering, and machine learning model development within a semiconductor R&D environment.

Required Qualifications:

Bachelor's Degree in Electrical / Electronics / Computer Engineering or equivalent

Preferred Qualifications:

Experience in programming languages, eg. Python, C/C++

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