PhD in Scalable Safe AI for Semiconductor Metrology

Introduction

Push multimodal GenAI beyond the lab. Join our research team as a PhD candidate to work on beyond the state-of-the-art model distillation and robustness methods enabling efficient reliable inference for challenging real-world problems in the semiconductor industry.

Job Description

Industry context and motivation.Pretrained foundation models bring lots of potential in semiconductors metrology. Multimodal foundation models combining data from multiple metrology sources enable high accuracy in the early stages of next generation manufacturing process and across many different usecases. As the process matures and enters high volume manufacturing these models can be specialized through efficient fine-tuning and distillation to improve throughput in terms of both compute and data acquisition while maintaining guaranteed performance on critical failure modes.

This PhD project focuses on developing data-efficient methods for fine-tuning and distillation under strict (customer fab) data and privacy constraints while providing performance guarantees for specific downsteam tasks.

Customer data can be indeed highly particular customers might not allow data to leave the fab and datasets that are available can be limited and imbalanced.

The model performance requirements are expected to evolve over time from early research to high-volume manufacturing. ML models should be able to adapt to changing accuracy speed and defect-detection requirements across the full lifecycle of a process node and should cover a wide variety of use cases across different customers with minimal adaptation.

Developing beyond state-of-the-art techniques enabling such ML behaviour of ML would have a profound impact facilitating more robust metrology models and significantly faster time-to-recipe across the maturity stages of a semiconductor process node.

Research settings:

PhD candidate will be formally employed with the Data and AI cluster at the Department of Mathematics and Computer Sciece and supervised by . Mykola Pechenizkiy and dr. Ghada Sokar. The project is done in tight collaboration with dr. Jan Jitse Venselaar and dr. Jacek Kustra of the ASML AI Research Team. The PhD candidate is expected to structurally spend time at both TU/e and ASML locations.

The project is part of NWO TTW Perspectief funded research program Foundation for Industry (FIND) - Large AI models for a resilient high-tech industry providing further opportunities for collaboration.

The project has access to the national computing infrustracture TU/e HPC cluster SPIKE-1 ASML HPC cluster ASML datasets and potentially custom data through collaboration with e.g. IMEC.

Job Requirements

• A masters degree AI Machine Learning Data Science Computer Science or a closely related field.
• Solid background in machine learning.
• Strong programming skills for machine learning.
• Enthusiastic and motivated in application inspired ML research
• Interest in collaborating with industrial partners.
• Experience in model distillation model adaptation and related topics is a plus.
• Good academic writing and communication skills.
• Fluent in spoken and written English (C1 level).

Conditions of Employment

A meaningful job in a dynamic and ambitious university in an interdisciplinary setting and within an international network. You will work on a beautiful green campus within walking distance of the central train addition we offer you:

• Full-time employment for four years with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme paid pregnancy and maternity leave partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities scale P (min. 3059 - max. 3881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure on-campus childrens day care and sports facilities.
• An allowance for commuting working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

About us

Eindhoven University of Technology is a leading international university within the Brainport region where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact today and in the future. TU/e is home to over 13000 students and more than 7000 staff forming a diverse and vibrant academic community.

With over 110 (assistant associate and full) professors almost 300 PhD and EngD students about 1500 Bachelor students and 1000 Master students the Department of Mathematics and Computer Science (M&CS) is the largest department of the TU/e. By performing top-level fundamental and applied research and maintaining strong ties with industry M&CS aims to contribute to science and innovation in and beyond the region.

Information

Do you recognize yourself in this profile and would you like to know more Please contact the hiring manager: Mykola Pechenizki .

Visit our website for more information about the application process or the conditions of employment. You can also contact HR services M&CS .

Curious to hear more about what its like as a PhD candidate at TU/e Please view the video.

Are you inspired and would like to know more about working at TU/e Please visit our career page.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
  We encourage applicants to reflect on three guiding research questions: (1) How can multimodal foundation models combining data from different metrology sources be designed to generalize across customers process nodes and use cases (2) How can pretrained models be efficiently fine-tuned using limited and highly imbalanced customer data while maintaining strong performance on rare but critical defect classes (3) How can multimodal models be distilled into single-modality models to improve throughput while preserving performance guarantees on underrepresented classes or populations
• Curriculum vitae including a list of your projects publications and any other relevant items
• A copy of or a link to your MSc thesis or an example of your academic writing if the MSc thesis cannot be shared.
• The contact information of two references.

Ensure that you submit all the requested application documents. We give priority to complete applications.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Please note

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.