PhD in automated test generation and self-testing for analog/mixed-signal integrated circuits
Offer DescriptionMany 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.RequirementsResearch Field Engineering Education Level Master Degree or equivalentLanguages ENGLISH Level GoodAdditional InformationBenefitsThe position offers :
Eligibility criteria
Selection processFor more information please contact Prof. dr. ir. Georges Gielen, mail: , tel.: +32 16 32 40 76.
You can apply for this job no later than 23/05/2024 via theWork Location(s)Number of offers available 1 Company/Institute KU Leuven Country Belgium State/Province Vlaams Brabant City Leuven Postal Code 3000 Street Leuven GeofieldWhere to apply WebsiteContact State/ProvinceLeuven CityVlaams Brabant StreetLeuven Postal Code3000STATUS: EXPIRED
Louvain, Brabant Flamand
Sat, 06 Apr 2024 07:55:30 GMT
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