Automated characterization of Read Out Integrated Circuit (ROIC) thin-film photodiode image sensor
Master internship - LeuvenPosted Oct 17, 2026
Overview
Skills named in this posting
- Python
The
Challenge Thin-film photodiode (TFPD) devices based on active materials,
such as quantum dots, organic layers and perovskites, are constantly being
developed and improved at imec to enable novel sensing capabilities beyond
conventional silicon technology. The active material properties are characterized by the
process engineers who develop the photodiode structure in the lab and evaluate
device performance on large, millimeter-sized single contacts. However, image
sensors contain hundreds of thousands of micrometer-scale pixels that operate
under conditions very different from laboratory test structures. As a result,
processing-lab measurements do not translate directly to image sensors, where
different properties matter and different characterization methods are
required. Moreover, since the characterization can take a long time, it is
important to automate the measurements where possible. In this project we aim to expand the suite of
characterizations that are available to evaluate the performance of the image
sensors. We will develop procedures for image lag, blooming and MTF. Other
metrics could be included depending on the progress of the student. What You
Will Do
Learn
about the performance indicators of image sensors , including
industry standards such as EMVA1288 and more custom methods.
Implement
measurement procedures, integrating the code into our existing
measurement software written in Python.
Write
scripts and maintainable code to analyse the measurements and present
the results in clear graphs and metrics.
Develop
the required hardware (PCB, mechanical, test setup) to perform the
measurements in an automated manner.
Apply
the new methods to our library of image sensors with a wide range of
materials, wavelengths and performances. Requirements Programming knowledge, preferably Python Time distribution 40% development, 40% lab, 10% literature study,
10% reporting Type of internship : Master internship Duration : 6-9 months Required educational background : Electrotechnics/Electrical Engineering, Nanoscience & Nanotechnology University promotor : Jan Genoe (KU Leuven) Supervising scientist(s) : For further information or for application, please contact Tristan Weydts ( Tristan.Weydts@imec.be ) The reference code for this position is 2026-INT-220 . Mention this reference code in your application. Imec allowance will be provided for students studying at a non-Belgian university. Applications should include the following information: resume motivation current study Incomplete applications will not be considered
Apply on the employer's site
Original posting: imec-int.com
RISC-V ecosystem
imec in the RISC-V ecosystem
RISC-V International: Community
Interuniversity Microelectronics Centre is an international research & development organization, active in the fields of nanoelectronics and digital technologies.
- RISC-V research/design: system-level integration of a digital AI accelerator with RISC-V
- Participates in RISC-V automotive and space-chip ecosystem work
Same employer
More roles at imec
- Research & development - LeuvenOct 28, 2026
- Research & development - EindhovenOct 28, 2026
- Engineering - LeuvenOct 28, 2026
- Research & development - LeuvenOct 28, 2026
- Engineering - DohaRTL / HDLPhysical designSoC / ASICOct 27, 2026
- Computer and IT - Gent ZwijnaardeOct 27, 2026