Master Thesis - Optical projection models using machine learning

About Mycronic

Mycronic is a global hightech company whose innovative solutions have been advancing electronics technology for over 40 years where the product lineup includes mask writers. Today we continue to grow and serve customers in an expanding variety of industries. What we do impacts the future of technology and in turn the way we live our lives tomorrow.

At this moment there is no other company in the world that can compete with Mycronics mask writers. All displays you see whether it is your TV laptop screen smart watch or infotainment screen in your car are manufactured with the use of Mycronics machines. The same technology is also used in the semiconductor industry. The process for the manufacturing happens at a nanometer level smaller than blood cells and viruses. For all of this to be possible a great emphasis is put on the quality of software and hardware.

Background

An important aspect in the manufacturing of lithographic masks at nanometerscale precision is the ability to verify that the patterngenerating equipment matches the expected performance. In such verification we often use numerical simulations of physics and we are currently looking into extending current simulation capabilities into models based on machine learning 1.

Project description

Diploma work at master 30 hp) level where the project will focus on the application of machine learning locked to physical principles. The project focus on a purely theoretical study of optical projection models possibly also leaving the scalar domain and entering vector electromagnetic propagation 2.

The outline of the project is as follows:

1. Study of the available literature and decision of project scope and suitable machine learning model.
2. Implementation and training of machine learning models for optical wave propagation following classical electrodynamics.
3. Evaluation of model performance with possibility of validation against finiteelement models (FEM) if found feasible.
4. Writing your MSc Thesis followed by an oral presentation of your results. During the diploma work there will be plenty of opportunities for getting acquainted also with experiments and ongoing product development at Mycronic.

While the work is carried out at Mycronic the study is of a generic nature and we will strive to publish any scientific outcome in peerreviewed academic journals. The location for the diploma work is preferably at Mycronics headquarters in Tby; however remote work also works perfectly well.

Your profile

We are looking for one or two candidates who enjoy computerassisted problem solving with stateoftheart machine learning models. As this project is tightly associated to physical principles and Maxwells equations you are probably in the final stage of finishing your undergraduate studies at engineering physics (F) or electrical engineering (E) and would like to leave the path open whether to enter a corporate career or to continue with PhD studies. The scope of your project will depend somewhat on your profile and background but it is preferable that you have a solid interest in machine learning and theory. In the project we target implementation of the model in Python using PyTorch or TensorFlow.

Contact and further information

Fredrik Jonsson Core Tech and Innovation Mycronic ; also at ngstrmlaboratoriet Division for Electricity Uppsala University

References

1 J. Lim and D. Psaltis MaxwellNet: Physicsdriven deep neural network training based on Maxwells Equations Appl. Phys. Lett. Photonics 7 . Max Born & Emil Wolf Principles of Optics (Cambridge University Press 2013.

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