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PhD in automated test generation and self-testing for analog/mixed-signal integrated circuits (ref. BAP-2024-212)

You will work in a very dynamic and innovative environment. The PhD supervisor is Prof. Georges Gielen, who is a worldwide expert in the design and design automation of analog/mixed-signal integrated circuits. The project will run in collaboration with industry for the demonstrator circuits used. The work will be performed in the ESAT-MICAS (Microelectronics and Sensors) research group at the Department of Electrical Engineering (ESAT) at KU Leuven, Europe’s most innovative university (Reuters, since 2016 till now). ESAT-MICAS is internationally renowned for its wide range of research, education and valorisation activities in integrated electronics. MICAS has over 80 researchers (postdocs and PhDs) from many different countries and offers a dynamic, thriving and interdisciplinary environment on a wide portfolio of research projects.

Unit website: https://micas.esat.kuleuven.be...


Many emerging electronic applications, such as smart autonomous systems (e.g. cars) and the internet of things (IoT), rely on analog and mixed-signal (AMS) circuits to interface with the physical world (e.g. sensors, radios, etc.). Examples are fields like automotive, biomedical, industry 4.0/5.0, etc. These applications however come with extremely demanding reliability and robustness requirements: no defective chips may be delivered to customers and no circuits may fail undetectedly. This must be addressed at IC design time (pre-fabrication), at IC test time (post-fabrication) and at execution run time (during IC usage). Due to their nature and sensitivity, however, the analog parts of AMS systems require significantly more testing effort than the digital parts.

This PhD will investigate and explore the emerging capabilities of developing and applying novel algorithmic techniques towards the efficient and automated test generation for analog integrated circuits. This includes exploring techniques from machine learning (ML) and artificial intelligence (AI). Self-testing and detection of outlying behavior will be a major focus to maximize test coverage further. Self-test solutions towards real-time monitoring and on-chip signal interpretation in the IC will also be investigated. Reuse of analog IP blocks and the corresponding test programs will be a solution to master the design complexity. The PhD work will involve the development and application of novel algorithms and computer-aided techniques, including AI/ML, to the testing and test generation of analog/mixed-signal integrated circuits in close collaboration with an industrial partner.


  • Required background: a Master degree in Electrical/Electronic/Nanotechnology Engineering (or equivalent) is required, with proven knowledge of analog/mixed-signal integrated circuits (IC) as well as with some knowledge of programming/algorithmic tools (preferrably including knowledge of AI/ML techniques). A Master degree in Computer Science with proven knowledge in algorithms/AI/ML methods and knowledge of (analog) electronics is acceptable too.
  • Candidates should be motivated, independent, show critical thinking and scientific curiosity.
  • Candidates should have strong team-player skills.
  • Excellent proficiency in the English language is required, as well as good communication skills, both oral and written.


The position offers :

  • A PhD scholarship for 4 years, with a very competitive monthly stipend;
  • An exciting interdisciplinary research environment in the largest research department at KU Leuven, Europe’s most innovative university;
  • The possibility to participate and present your research work in international conferences and collaborations;
  • The possibility to interact and collaborate with industry.


For more information please contact Prof. dr. ir. Georges Gielen, mail: georges.gielen@kuleuven.be, tel.: +32 16 32 40 76.

KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address: hr.diversiteit@kuleuven.be



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