- 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.
- 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.
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PhD in automated test generation and self-testing for analog/mixed-signal integrated circuits - Leuven, België - KU Leuven
Beschrijving
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.
The position offers :
