Leuven | More than two weeks ago
Introduction
In recent years, short-wave (SWIR) and mid-wave (MWIR) infrared technologies have gained traction in various fields, including healthcare, agriculture, security (such as face recognition and surveillance), automotive, machine vision, and virtual reality (VR). Among the leading technologies for SWIR detection are photodetectors based on quantum-dot (QD) thin films, valued for their cost-effective, high-resolution performance and tunability to specific wavelengths. However, these QDs often contain lead or other heavy metals, which pose significant health risks. As a result, developing heavy metal-free alternatives for SWIR or MWIR sensors is crucial to addressing these concerns and advancing efficient, tunable, and environmentally friendly imaging technologies.
Additionally, we propose the use of black phosphorus (bP) as the active layer for this new generation devices. As a layered material, bP allows precise thickness control from monolayer to bulk. Its high mobility and clean interfaces can lead to faster device performance compared to current QD-based technologies.
Topic
The goal of this PhD is to explore and develop black phosphorus-based thin absorbers for SWIR and MWIR wavelengths and integrate them in imec state-of-the-art imagers.
Layered bP thin films will be produced via chemical exfoliation or ink process, with their thickness determined by the material's optical properties, as the bP bandgap is tunable by the number of layers. The photodiode stack and configuration will be determined based on electrical and optical simulations. Student will learn to fabricate and characterize the devices and feedback will be used to optimize their performance. Electrical stability will be evaluated on regular basis. Finally, the photodiode stack will be integrated within imec imager platform for state-of-the-art SWIR and MWIR imagers.
Required background: Nano-engineering, materials science, chemistry, physics, electrical engineering
Type of work: 10% literature study, 20% design/modeling, 40% processing, 30% characterization
Supervisor: Jan Genoe
Daily advisor: Isabel Pintor Monroy, Sai Gourang Patnaik
The reference code for this position is 2025-112. Mention this reference code on your application form.