Machine-learning/AI-based testing and test generation for analog/mixed-signal ICs

Katholieke Universiteit Leuven

Belgium

December 24, 2021

Description

Machine-learning/AI-based testing and test generation for analog/mixed-

signal ICs

(ref. BAP-2020-1002)

Last modification : Tuesday, November 23, 2021

The PhD research will be carried out under the guidance of Prof. Georges Gielen (https: // www. esat.kuleuven.be/micas/index.php/georges-gielen), who is an expert in the design, design automation and testing of analog/mixed-signal integrated electronic circuits. 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 valorization activities in integrated electronics (https: // www. esat.kuleuven.be/micas/). MICAS has over 80 researchers (postdocs and PhD students) from many different countries and offers a dynamic, thriving and interdisciplinary environment on a wide portfolio of research projects.

Website unit

Project

A PhD position is available within the frame of a VLAIO project of Prof. Georges Gielen in collaboration with an automotive company. Analog and mixed- signal electronic circuits are essential in many applications like Internet of Things, biomedical, automotive, among many others. Especially in safety- critical applications like automotive, there are extremely tight requirements on the reliability and robustness of the integrated circuits (ICs): the circuits may not fail, and any defects introduced during fabrication must be detected during the testing of the ICs. Since the current techniques used in industry do not reach the required parts-per-billion (ppb) test escape levels, novel test techniques must be developed to address this problem.

This job opening covers a PhD research position (4 years) in the frame of this VLAIO project. The candidate will investigate and explore a novel techniques to improve the quality and test coverage of test methods for analog and mixed-signal integrated circuits. Focus will be on investigating and applying advanced statistical and machine-learning/artificial-intelligence- based techniques. Also, solutions towards real-time on-chip monitoring and signal interpretation will be investigated.Second goal is to reduce the time needed for analog/mixed-signal test program development through novel techniques for automated test signal generation. The candidate will have to prototype and validate the methods on actual designs in collaboration with the industrial partner.

Profile

Candidates should have a strong expertise in (analog/mixed-signal) IC design and computer algorithms (in particular methods of optimization, statistics and/or machine learning/artificial intelligence).

Additional research/development experience in any of the following topics is a plus:

  • hands-on experience in programming
  • hands-on experience in design of (analog/mixed-signal) integrated circuits
  • hands-on experience with methods of optimization and/or machine learning/AI
  • Candidates should be motivated, independent, show critical thinking and scientific curiosity, and should have strong team-player skills.

    Required background: Master in Electrical Engineering, Master in Nano Engineering or Master in Computer Science with proven knowledge of analog/mixed-signal integrated electronic circuits besides the mastering of programming/CAD/AI techniques.

    Excellent proficiency in the English language is required, as well as good communication skills, both oral and written.

    Offer

    The position offers :

  • A PhD scholarship for 4 years, with a competitive monthly stipend;
  • An exciting interdisciplinary research environment at KU Leuven, Europe's most innovative university;
  • The possibility to participate in international conferences and collaborations;
  • The possibility to collaborate with industry.
  • Interested?

    Interested applicants should submit a motivation letter with a statement why this project fits with your expertise, a curriculum vitae, the names and contact information of 2 references.

    Please do not postpone submitting your application until the deadline. Applications are monitored continuously, and it could be that the vacancy closes before its end date once a candidate has been found.

    For more information please contact Prof. dr. ir. Georges Gielen, tel.: +32 16 324076, mail: georges.gielen@kuleuven.be .

    KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.

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