Design-aware nanophotonic substrates for surface-enhanced, ultrafast spectroscopy of EUV photoresists
Master internship - LeuvenPosted Oct 17, 2026
Overview
Skills named in this posting
- Python
Extreme ultraviolet (EUV) lithography serves as the bedrock patterning
technology that produces the world’s most advanced nanoelectronic structures and
devices. While EUV lithography is already adopted in high-volume manufacturing of
advanced chips, the underlying physical and chemical mechanisms of how EUV
photons transform light-sensitive materials (photoresists) into nanoscale
circuitry patterns remain elusive. The mysteries of EUV reaction mechanisms in
nanometer-scale thin films have their origins in the highly energetic and
complex reaction landscape of EUV lithography, which involves (among others)
ionization, formation of transient species, electron interactions, and chemical
reactions. Resolving this complex reaction landscape within the confined volume
of nanometer-scale films and with a time-resolution to “see” the transformation
in real time is a scientific grand challenge. In imec’s AttoLab, we are tackling this grand
challenge by developing femtosecond time-resolved spectroscopy to track EUV
photoresist dynamics in real-time. However, the limited interaction volume of
at-scale EUV photoresists (<30 nm thickness) severely degrades the signal-to-noise
ratio (SNR) and prevents detection of key molecular events occuring during the
exposure process. In this project, you will help to tackle the SNR challenge by
exploiting the benefits of localized surface plasmon resonances (LSPR’s) of
nanostructured arrays to enhance spectroscopic signals (i.e., surface-enhanced infrared
absorption spectroscopy, SEIRA). Working closely with imec experts in
lithography, materials chemistry, and ultrafast optical science, you will
design and simulate nanostructured arrays that support LSPR’s to boost IR
signals at key reactive modes for EUV photoresists. The design and simulations
will be performed using finite element or finite-difference time-domain methods,
which take into account realistic material properties and CMOS compatibility. You
will then translate the simulation design and results into a real-world process
flow that will be used to produce the substrates. Time permitting, you will
also have the opportunity to characterize the designed and produced substrates
using a suite of microscopies (e.g., AFM, SEM, etc.) and spectroscopies (e.g,
FTIR). Who you
are: Motivated student with a
passion for (nano)optics, data analysis, and advanced simulations In-progress Masters’s degree in
chemistry, physics, material science, nanoscience (or comparable) Solid background on finite
element or finite difference time domain modelling approaches, spectroscopy,
and/or nano-optical components Proficiency
in data analysis and coding (Python, Matlab, etc.) is highly valued Good written and verbal English
skills Type
of work: 10% data analysis, 80%
modeling/simulation, 10% literature Type of internship : Master internship Duration : 3 to 6 months Required educational background : Chemistry/Chemical Engineering, Materials Engineering, Physics, Nanoscience & Nanotechnology University promotor : Pol Van Dorpe (KU Leuven) Supervising scientist(s) : For further information or for application, please contact James Rupert ( James.Rupert@imec.be ) and Kevin Dorney ( Kevin.Dorney@imec.be ) The reference code for this position is 2026-INT-345 . 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
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