PhD - Gent | More than two weeks ago
Model, design, build and test photonic chips (combining lasers, modulators and detectors) for LiDAR systems with coherent detection to enable the autonomous vehicles of the future.
LiDAR is considered an essential enabling technology for the future of mobility, both for autonomous driving, as well as efficient use of low air space by drones and UAVs. Today’s LiDAR systems are quite primitive, and make use incoherent detection methods, which makes them vulnerable for crosstalk and interference. Especially in future scenarios where the surroundings are populated with many other LiDAR emitters, The LiDAR ranging and beam scanning engine should be robust against direct ‘attacks’.
Coherent LiDAR is one of the techniques that can help here. Instead of just detecting the reflected photon pulses, the incoming signal is coherently mixed with a local oscillator, which creates a well-defined beat tone that can be used for detecting object distance and velocity. This mixing acts as a strong filter that rejects a lot of the unwanted crosstalk from unrelated sources.
There are different methods for implementing coherent LiDAR (and more particular the coherent ranging that does the distance detection), but in this PhD you will look at solutions based on photonic integrated circuits. These are chips which contain multiple optical elements (e.g. lasers, modulators, detectors, …) connected together by optical waveguides. Such a photonic circuit can take care of the light generation in multiple laser sources, multiplexing them together, and couple them to free space using on-chip antennas. In this PhD, the various elements of such a system will be studied, but the focus will be on the ranging and the antennas themselves. In particular, the work will involve
The PhD candidate is expected to have a good knowledge of photonics. Basic electronics know-how is a valuable asset, and programming skills (Python) are also required.
Required background: Photonics, signal processing, microwave
Type of work: 40% modeling, 30% design, 30% experimental
Supervisor: Wim Bogaerts
Co-supervisor: Marcus Dahlem
Daily advisor: Wim Bogaerts
The reference code for this position is 2024-137. Mention this reference code on your application form.