Master Thesis: Advancing Ion Beam Tuning Prediction in Semiconductor Manufacturing

Explore the cutting-edge realm of semiconductor manufacturing! Your thesis will revolve around ion implantation equipment. Your mission: develop an AI model that goes beyond predicting just the subsequent tuning outcome, foreseeing multiple upcoming tunings.

Your Challenge

In semiconductor manufacturing, ion beam tuning is critical for each process specification change at implantation.1 The key to success lies in avoiding costly timeouts caused by unsuccessful tuning. As part of your thesis, you’ll work on an AI prediction model that estimates the tuning outcome for the consecutive implantation process as the first step. Building on this model, the final goal is to extend it to predict outcomes for multiple upcoming tunings.

Deep Learning in Focus

Over the course of an 8–12-month internship, you’ll dive into the realm of deep learning models. Within a team of master and PhD students, you will be developing cutting-edge techniques that empower predictive maintenance, revolutionizing semiconductor processes.

Your Profile

As a passionate computing and data enthusiast, you’re the perfect candidate for this exciting challenge. Your solid experience with Python, including scikit-learn and TensorFlow/Keras/Pytorch, makes you wellprepared for the task. Fluency in written and spoken English is essential, with German language skills as a plus. While not mandatory, familiarity with SQL will be advantageous. The ability to query data from databases will enhance your exploration of predictive insights.

Join the Frontier of Ion Beam Tuning Prediction!

Embrace the opportunity to be at the forefront of semiconductor advancements. Your contributions to developing an AI model that predicts multiple upcoming tunings will shape the future of manufacturing.

Our company offers a flexible work environment, allowing remote work from home. Employees can also dedicate office hours to academic pursuits, including thesis writing. Join us to thrive personally and professionally.

This position is subject to the collective agreement for workers and employees in the electrical and electronics industry (full-time), employment group D (basic level) for master students:


Preferred start date: October 2023

Please attach the following documents (German or English) to your application and send it to andreas [dot] laber [at] infineon [dot] com:

  • CV
  • Letter of motivation
  • Certificate of matriculation in a master program at a university
  • Latest transcript of records (not older than six months)


Invitation Talk: “Towards Large Neural Networks that can Reason”

The Italian Association for Artificial Intelligence is pleased to announce the next seminar of its Spotlight Seminars on AI initiative:

April, 20 – 5:00PM (CEST)

Title: Towards Large Neural Networks that can Reason

Speaker: YOSHUA BENGIO, Université de Montréal

The aim of the seminar series is to illustrate, explore and discuss current scientific challenges, trends, and possibilities in all branches of our articulated research field. The seminars will be held virtually on the YouTube channel of the Association (, on a monthly basis (and made permanently available on that channel), by leading Italian researchers as well as by top international scientists.

The seminars are mainly aimed at a broad audience interested in AI research, and they are also included in the Italian PhD programme in Artificial Intelligence; indeed, AIxIA warmly encourages the attendance of young scientists and PhD students.

Bio: Yoshua Bengio is recognized worldwide as one of the leading experts in artificial intelligence, known for his conceptual and engineering breakthroughs in artificial neural networks and deep learning. He is a Full Professor in the Department of Computer Science and Operations Research at Université de Montréal and the Founder and Scientific Director of Mila – Quebec Artificial Intelligence Institute, one of the largest academic institutes in deep learning and one of the three federally-funded centers of excellence in AI research and innovation in Canada. He began his studies in Montreal, where he obtained his Ph.D. in Computer Science from McGill University in 1992. After completing a postdoctoral fellowship at the Massachusetts Institute of Technology (MIT) on statistical learning and sequential data, he completed a second postdoc at AT&T Bell Laboratories, in Holmdel, NJ, on learning and vision algorithms in 1993. That same year, he returned to Montreal and joined UdeM as a faculty member. In 2016, he became the Scientific Director of IVADO. He is Co-Director of the CIFAR Learning in Machines & Brains program that funded the initial breakthroughs in deep learning and since 2019, holds a Canada CIFAR AI Chair and is Co-Chair of Canada’s Advisory Council on AI. In 2022, Yoshua Bengio became the most cited computer scientist in the world (h-index). Concerned about the social impact of AI, he actively took part in the conception of the Montreal Declaration for the Responsible Development of Artificial Intelligence. His goal is to contribute to uncovering the principles giving rise to intelligence through learning while favouring the development of AI for the benefit of all. Yoshua Bengio was made an Officer of the Order of Canada and a Fellow of the Royal Society of Canada in 2017 and in 2020, became a Fellow of the Royal Society of London. From 2000 to 2019, he held the Canada Research Chair in Statistical Learning Algorithms. He is a member of the NeurIPS Foundation advisory board and Co-Founder of the ICLR conference. His scientific contributions have earned him numerous awards, including the 2019 Killam Prize for Natural Sciences, the 2017 Government of Québec Marie-Victorin Award, the 2018 Lifetime Achievement Award from the Canadian AI Association, the Prix d’excellence FRQNT (2019), the Medal of the 50th Anniversary of the Ministry of International Relations and Francophonie (2018), the 2019 IEEE CIS Neural Networks Pioneer Award, Acfas’s Urgel-Archambault Prize (2009) and in 2017, he was named Radio-Canada’s Scientist of the Year. He is the 2018 laureate of the A.M. Turing Award, “the Nobel Prize of Computing,” alongside Geoffrey Hinton and Yann LeCun for their important contributions and advances in deep learning. In 2022, he was appointed Knight of the Legion of Honor by France and named co-laureate of Spain’s Princess of Asturias Award for technical and scientific research.

Abstract: Current neural networks, such as large language models and those based on images or paired images and text, are trained to fit their training data, with very little in their architecture that could force them to produce answers that are coherent with respect to individual pieces of knowledge. In that sense, they seem to be missing some of the reasoning abilities and causal understanding that humans can benefit from, and this may result in incoherent outputs and mistakes that humans would typically not make, especially out-of-distribution. This raises the larger question of how higher-level cognitive abilities could be incorporated in deep learning. We know a lot from neuroscience and cognitive science about them and that can be used to design new architectures and training frameworks with the corresponding inductive biases. This has motivated a novel form of deep learning called generative flow networks or GFlowNets, borrowing from reinforcement learning, generative models and amortized variational inference. GFlowNets can sequentially generate compositional data structures whose content may be analogous to our thoughts, and they can be trained to sample them with probability proportional to some given or learned reward function that corresponds with the coherence of the context and generated answer with a structured world model. A GFlowNet can thus be trained to perform amortized probabilistic inference that is consistent with the pieces of knowledge of the world model, including in the sense of generating samples from a Bayesian posterior over world models. Like with amortized variational methods, this can be used to learn the world model itself. That arrangement is similar to model-based reinforcement learning (where we separate the policy from the world model) but concerns the learning of a policy that chooses what internal computation (i.e. reasoning) to perform, rather than acting in the world. Unlike with state-of-the-art deep learning and reinforcement learning, this makes it easy to incorporate inductive biases about high-level cognition and causality in the world model itself, such as sparse causal dependencies and reusable modular pieces of knowledge. It means that the GFlowNet probabilistic inference machine can be trained by querying the world model, without having to directly interact with the real world, and can be as large and trained with as many queries as our computational capabilities allow: unlike current deep nets, its effective capacity is not limited by the size of the externally observed data. This is convenient because probabilistic inference is generally intractable and may thus require high capacity in order to be approximated with a fast neural net. The mathematical foundations of GFlowNets and how they constitute an interesting ML-based alternative to MCMC inference will be briefly explained and recent work on GFlowNets highlighted.

Decoration of Honour of the Province of Carinthia for Univ.-Prof. Gerhard Friedrich

For his special services as a professor and in particular as the longstanding Dean of the Faculty of Technical Sciences at the University of Klagenfurt, Univ.-Prof. DI Dr. Gerhard Friedrich was awarded the Decoration of Honour of the Province of Carinthia by Governor Dr. Peter Kaiser on 18 July 2022 in the Mirror Hall of the Carinthian Provincial Government.

Mr Friedrich has worked persistently and highly successfully for a focus of Carinthia as an educational, scientific and business location in the field of technical sciences and digitalisation. Particularly noteworthy in this context is his great commitment in the field of artificial intelligence applications. In addition, he was able to successfully promote and expand networking between the Austrian economy and the University of Klagenfurt. Thanks to his initiative and tireless efforts, a significant expansion of the technical sciences in terms of personnel and content has been achieved, especially in the area of cybersecurity and modular robot systems.

During his tenure, among other things, the computer science workshop, the establishment of a drone hall, the establishment of the Silicon Austria Lab (SAL) and successful and promising cooperations with the research institution Joanneum Research, newly settled in Carinthia, were created. He has consistently pursued the orientation of the Faculty of Technical Sciences towards international visibility and scientific excellence, thus enabling highly gratifying results in the university rankings.

We warmly congratulate Univ.-Prof. Gerhard Friedrich!


Governor Peter Kaiser presents the Decoration of Honour of the Province of Carinthia to Gerhard Friedrich | Photo: LPD Kärnten/Eggenberger

Two research teams from the University of Klagenfurt remain in the running to be “Clusters of Excellence”

As recently announced by the Austrian Science Fund FWF, eleven teams have reached the final stage of selection for “Cluster of Excellence” funding. The decisions on Austria’s future beacons of basic research will be made in early 2023. The eleven consortia include the “Multi-drone Systems” cluster initiated and led by the University of Klagenfurt, as well as the “Bilateral Artificial Intelligence” project, which feature scientists from Klagenfurt on their Board of Directors.

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