Publications
Magnetic measurement methods to probe nanoparticle-matrix interactions Ghent University
Magnetic nanoparticles (MNPs) are key elements in several biomedical applications, e.g., in cancer therapy. Here, the MNPs are remotely manipulated by magnetic fields from outside the body to deliver drugs or generate heat in tumor tissue. The efficiency and success of these approaches strongly depend on the spatial distribution and quantity of MNPs inside a body and interactions of the particles with the biological matrix. These include dynamic ...
Estimating the heating of complex nanoparticle aggregates for magnetic hyperthermia Ghent University
Understanding and predicting the heat released by magnetic nanoparticles is central to magnetic hyperthermia treatment planning. In most cases, nanoparticles form aggregates when injected in living tissues, thereby altering their response to the applied alternating magnetic field and preventing the accurate prediction of the released heat. We performed a computational analysis to investigate the heat released by nanoparticle aggregates featuring ...
The impact of temperature on thermal fluctuations in magnetic nanoparticle systems Ghent University
We investigate the effect of temperature on the thermal magnetic noise signal of magnetic nanoparticle (MNP) systems as models for non-interacting macrospins. An analytical expression for the amplitude of the fluctuations in a magnetic field is derived for the Brownian and the Neel fluctuation mechanisms and compared with numerical results at different temperatures. To experimentally validate our findings, magnetic noise spectra of two ...
Monitoring magnetic nanoparticle clustering and immobilization with thermal noise magnetometry using optically pumped magnetometers Ghent University
Thermal noise magnetometry as an emerging magnetic characterization technique Ghent University
This contribution gives an overview of recent advances in thermal noise magnetometry (TNM), an emerging method for the characterization of magnetic nanoparticles. This method is unique as it does not rely on measuring the response to an external field excitation. Instead it passively measures the fluctuations in the magnetization of the particle ensemble in thermal equilibrium. Through theoretical and experimental advances, we show how TNM can ...
SEMI-CenterNet : a machine learning facilitated approach for semiconductor defect inspection Ghent University
Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise and the dynamic behavior of defect patterns and types. Conventional rule-based methods and non-parametric supervised machine learning algorithms like k-nearest neighbors (kNN) mostly fail at the requirements of semiconductor defect inspection at these advanced nodes. ...
SEMI-DiffusionInst : a diffusion model based approach for semiconductor defect classification and segmentation Ghent University
With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection and accurate metrology extraction are two main challenges in advanced node (2 nm and beyond) technology. Deep learning (DL) algorithm based computer vision approaches gained popularity in semiconductor defect inspection over last few years. In this research work, a ...
Tutorial : simulating modern magnetic material systems in mumax3 Ghent University
This Tutorial article focuses on magnetic phenomena and material systems that have gained significant importance since the original development of mumax3, but are challenging to simulate for users who rely solely on the originally provided examples. Alongside the physical background, we provide hands-on examples of advanced magnetic systems, including detailed explanations of complete mumax3 input files (13 in total, often showing different ways ...
Spatial analysis of physical reservoir computers Ghent University
Physical reservoir computing is a computational framework that implements spatiotemporal information processing directly within physical systems. By exciting nonlinear dynamical systems and creating linear models from their state, we can create highly-energy-efficient devices capable of solving machinelearning tasks without building a modular system consisting of millions of neurons interconnected by synapses. To act as an effective reservoir, ...