Intelligent microsystems – Micro-sensors with in-sensor AI computing capabilities

About the Project

In the era of Internet-of-Things (IoT), there will be an astronomical amount of data generated from numerous sensors worldwide. It is extremely insecure, power-inefficient, and time-consuming to transfer all the data between sensor nodes and computing units frequently. To efficiently process this substantial amount of data, a novel paradigm termed “in-sensor computing” has been proposed recently [1].

Since MEMS sensors, which are already pervasive in modern-day technologies, are expected to be underpinning components for IoT, it is natural to consider implementing in-sensor computing capabilities with MEMS sensors.

In this highly interdisciplinary project, you will explore this nascent area and develop a novel approach to realise in-sensor computation based on neuromorphic computing approaches embedded with high-performance MEMS sensors. From this project, you will delve into several highly topical research areas, including but not limited to: (1) MEMS sensors for IoT; (2) bio-inspired, unconventional computing; (3) coupled nonlinear microstructures; (4) machine learning, etc.

This project will be jointly supervised by Dr Chun Zhao and Dr. Martin Trefzer. The supervisors have strong connections with both academia (e.g., University of Cambridge, Ulster, and Sheffield) and industry (e.g., ARM, IBM Zurich, IMEC). The supervisors encourage collaboration and academic exchanges, and you will have a plethora of opportunities for collaboration.

The student will access the well-equipped laboratories available at the University of York, and will gain comprehensive training and experience in a wide range of tasks essential to the project, e.g., MEMS sensor design and fabrication, bio-inspired computing modelling and hardware implementation. Full-time training for postgraduate students, tailored to their particular degree background, is provided within the active research groups.

Academic Requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electronic, Mechanical, and Control Engineering or a closely related subject. Applications will be considered on a competitive basis with regard to the candidate’s qualifications, skills, experience and interests.

How to apply:

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.

This project is open-ended making it suitable for MSc by Research and PhD level.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website View Website for details about funding opportunities at York.

References

[1] T. Wan, et al. "In‐Sensor Computing: Materials, Devices, and Integration Technologies." Advanced Materials (2022): 2203830.