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Thesis Work – Detection of deviations in photomasks using machine learning

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Website Mycronic

About Mycronic

Mycronic is a global high-tech 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 Mycronic’s 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 Mycronic’s machines. The same technology is also used in the semi-conductor 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 nanometer-scale precision is the ability to verify that the patterns match the expected performance. Much of this inspection and decision-making is today done manually, partly due to the history of development in the semiconductor industry, but also due to the ability and mandate of the tool operator to decide which deviations are to be considered as within or outside limits. In this aspect, machine learning offers a promising support functionality which removes individual judgment and possible errors.

Project description

Diploma work at master (30 hp) or candidate (15 hp) level, which will determine the extent of the project. The project focus on a purely theoretical study of classification schemes of deviations in microlithographic photomasks, based on simulated images where artificially added deviations, defects and noise are used as parameters mimicking real performance.

The outline of the project is as follows:

  • Study of the available literature and decision of project scope and which machine learning model to go for.
  • Implementation and training of machine learning models for classification of defects and deviations.
  • Evaluation of model performance as function of various defects with respect to size of deviations and noise.
  • 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 peer-reviewed academic journals. The location for the diploma work is preferably at Mycronic’s headquarters in Täby; however remote work also works perfectly well.

Your profile

We are looking for one or two candidates who enjoy computer-assisted problem solving with state-of-the-art machine learning models. You are probably in your final year of master studies in engineering physics (F), computer science (D), or electrical engineering (E); alternatively about to finish your candidate (Bachelor) in computer science. The scope of your project will depend on your profile and background. In the project, we target implementation of the model in Python using TensorFlow or PyTorch.

Contact and further information

Fredrik Jonsson, Core Tech and Innovation, Mycronic, [email protected]; also at Ångströmlaboratoriet, Division for Electricity, Uppsala University, [email protected].

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Website Mycronic

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