Graduation assignment: deep learning applied to SEM images
Research & Development
In a nutshell
Research & Development
No experience (Student)
Data Science, Mathematics, Physics
Are you a Master student in Applied Mathematics, Physics, Data science or Computer Science with experience in deep learning and Python? Then this assignment could be an interesting opportunity for you!
For the optimal fabrication of nanometer-sized semiconductor devices, regular feedback is required. A major part of this feedback consists out of images taken with a scanning electron microscope (SEM). There are various ways employed to extract the dimensions of nanometer-sized features from SEM images. The driving question behind this internship is: Can we extract other useful information from common SEM images?
We believe that deep-learning can extract more useful information from SEM images than conventionally extracted. Your assignment will be to help develop the next step in proving these ideas. More specific, we expect you to:
•Train a deep neural network with an encoder-decoder architecture using simulated SEM images as training data;
•Optimize the hyper-parameters of the network in order to improve the performance of the model on diverse categories of SEM images;
•Engage in discussions on how we could further deploy the results you obtain.
You are a Master student in the field of Applied Mathematics, Physics, Data Science or Computer Science. Familiarity with the theory of deep neural networks and their implementation is critical for this assignment. You should be well versed in Python and be interested to learn how SEM images are formed. We are looking for someone who is naturally curious, self-motivated and good at working independently. You should be comfortable presenting progress on your work on a weekly basis and actively participate in group meeting discussions. Furthermore, your communication skills in English should be excellent.
This is a Master’s apprentice/graduation internship for 4-5 days a week with duration of a minimum 5 months.
Please keep in mind that we can only consider students (who are enrolled at a school during the whole internship period) for our internships and graduation assignments.
What ASML offers
Your internship will be in one of the leading Dutch corporations, gaining valuable experience in a highly dynamic environment. You will receive a monthly internship allowance of 500 euro (maximum), plus a possible housing or travel allowance. In addition, you’ll get expert, practical guidance and the chance to work in and experience a dynamic, innovative team environment.
ASML: Be part of progress
We make machines that make chips – the hearts of the devices that keep us informed, entertained and safe; that improve our quality of life and help to tackle the world’s toughest problems.
We build some of the most amazing machines that you will ever see, and the software to run them. Never satisfied, we measure our performance in units that begin with pico or nano.
We believe we can always do better. We believe the winning idea can come from anyone. We love what we do – not because it’s easy, but because it’s hard.
Students: Getting ready for real-world R&D
Pushing technology further is teamwork, and our R&D team is more than 5,500 people strong, with major sites on three continents. Dozens of diverse, interdisciplinary teams work in parallel to meet a challenging development schedule.
In such an environment, your colleagues may be sitting next door, or they could be thousands of kilometers away in a different country, or even working for a different company.
An internship at ASML is your opportunity to get to know this world of industrial-strength R&D and get a feel for what excites you most. Will you design a part of the machine, or make sure it gets built to the tightest possible specifications? Will you write software that drives the system to its best performance, or work side-by-side with the engineers of our customers in a fab, optimizing a system to the requirements of the customer?
How will you be part of progress?