Master Thesis: SWIR PD and Functional Encapsulation for Hyperspectral Imager

This study focuses on the system-level exploration of a new image sensor architecture for short-wavelength infrared (SWIR) operation based on thin-film photodiodes (TFPDs). In typical TFPD devices, an encapsulation layer is required to ensure long-term reliability and environmental stability, protecting the photodiode from oxidation, moisture ingress, and mechanical damage. However, conventional encapsulation materials often introduce optical drawbacks, such as spectral distortion, parasitic reflection, or absorption, which can degrade sensor performance. In this work, the encapsulation layer is re-envisioned not as a passive protective coating, but as a functional multispectral optical filter. In parallel, the study will also investigate photodiode designs and material choices that are inherently suited for hyperspectral imaging in the SWIR range, emphasizing broadband responsivity, high quantum efficiency, and spectral stability to ensure that the sensor platform can fully exploit the benefits of the integrated filtering approach. 

By engineering parameters such as refractive index, thickness, interference effects, and potentially integrating nanophotonic structures, the encapsulation can be designed to spectrally shape incoming light at the pixel level. This enables each pixel to exhibit a distinct spectral response, making it possible to perform compact hyperspectral imaging without the need for bulky prisms, gratings, or mechanical scanning systems.

The scope of this research focus on system-level design, modeling, device concept rather than real fabrication. A device- and optics-aware forward model will be established to simulate the complete signal chain from scene spectra to pixel readout, incorporating filter response, TFPD EQE, and noise effects. This framework will be used to optimize filter shapes, determine the required number of distinct filters, and define their spatial arrangement across the array. In addition, the study will address filter optimization specifically for hyperspectral-domain compressive sensing, ensuring that the selected filter set maximizes spectral reconstruction accuracy for the intended applications. 

This interdisciplinary project lies at the intersection of device physics, materials engineering, image sensor pixel design, and computational imaging. Therefore, we are seeking a highly motivated candidate with a strong background in device fabrication, semiconductor process engineering, materials integration, optics and electrical engineering. Familiarity with TFPD photodetector materials, process integration, or optical simulation is a plus.

Candidates from electrical engineering, materials science, physics, or nanofabrication-related disciplines are especially encouraged to apply. The project offers full access to state-of-the-art cleanroom and characterization facilities and opportunities for collaboration with both academic and industry partners.

Type of Project: Thesis

Master's degree: Master of Engineering Technology

Master program: Electrotechnics/Electrical Engineering; Materials Engineering; Physics

Supervisor: Jan Genoe (EE, Nano)

For more information or application, please contact the supervising scientist Myonglae Chu (myonglae.chu@imec.be).