Automated characterization of Read Out Integrated Circuit (ROIC) thin-film photodiode image sensor

Imec; Vlaams-Brabant; In loco; EUR 13.000 - 20.000; Part-time

Vantaggi offerti da questo lavoro

Imec allowance for non-Belgian uni

Descrizione del lavoro

Imec in Belgium invites a Master student to work on automated characterization of ROIC thin-film photodiode image sensors. You will learn image sensor performance indicators and develop Python-based measurement procedures, integrating with existing software.

The internship combines development and lab work (40% development, 40% lab, 10% literature study, 10% reporting), lasts 6-9 months, and includes supervision by Jan Genoe (KU Leuven) and Tristan Weydts.

Competenze

• Programming knowledge, preferably Python.

Mansioni

• 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.

Conoscenze

Python

Formazione

Electrotechnics/Electrical Engineering, Nanoscience & Nanotechnology

Descrizione del lavoro

Automated characterization of Read Out Integrated Circuit (ROIC) thin-film photodiode image sensor

Help define the impact of imec's image sensor technologies for non-visible wavelengths.

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): Tristan Weydts ([e-mail])

The reference code for this position is 2026-INT-220.

Imec allowance will be provided for students studying at a non-Belgian university.