Upcoming Talks

Density-Functional Theory (DFT) has seen tremendous improvements in the accuracy of its implementations since its first inception. The theory is characterized by the Nobel Prize-winning insight that the number density of electrons uniquely determines the ground-state properties of a system of atoms without the need to evaluate the many-body wavefunction for the electrons. In this talk, I will discuss another key insight that when coupled to DFT, leads to exceptionally accurate predictions from first principles. The insight is that the Fermi level (the electronic chemical potential) behaves in some cases as a manifestly local quantity rather than as uniform throughout a crystal sample, an assumption commonly employed in materials computations. This insight can be used to accurately predict the measured values of electric fields probed using color centers in diamond with the aim of improving the functioning of semiconductor devices. The insight can also be used to accurately determine timescales for charge-state decay of ionized color centers in diamond with applications in quantum computation, quantum communication, and quantum sensing.

Rodrick Kuate Defo is an assistant professor in the Department of Electrical Engineering and Computer Science at Syracuse University. Prior to his current position, he was a postdoctoral research fellow in the Department of Electrical and Computer Engineering and a Visiting Faculty Fellow in the McGraw Center for Teaching and Learning at Princeton University. He completed his PhD in physics with a secondary field in computational science and engineering at Harvard University and earned his bachelor's degree in math and physics from McGill University.

Accurately predicting surface topography evolution during semiconductor processing is essential for advanced device manufacturing and Process/Design Technology Co-Optimization (DTCO). DTCO bridges semiconductor process development and circuit design, ensuring that manufacturing constraints, device performance, and power efficiency are optimized together. By integrating insights from process modeling into early design stages, DTCO helps enhance yield, reduce costs, and enable continued scaling of semiconductor devices. Feature-scale modeling plays a central role in this effort, as it connects reactor-scale process conditions, such as ion and neutral fluxes and their distributions, to the resulting material modifications at the nanoscale. In this talk, we present ViennaPS, a flexible and efficient framework for simulating topography evolution during etching and deposition, enabling predictive process design and optimization. To improve model accuracy, ViennaPS incorporates atomistic-scale insights (DFT/MD), which help characterize fundamental surface reactions, such as adsorption, desorption, and sputtering. These reaction mechanisms, in turn, define surface evolution models used in feature-scale simulations. Chamber-scale plasma simulations provide spatially resolved flux distributions of reactive species, ensuring that feature-scale models reflect the local process conditions imposed by reactor design and operating parameters. Beyond physics-based modeling, we explore the automated extraction and optimization of model parameters from SEM/TEM images, where experimental feature profiles guide the refinement of topography models, reaction rates, and material-specific properties. Additionally, equipment-scale surrogate models can be integrated into ViennaPS to incorporate realistic plasma reactor effects while maintaining computational efficiency. This multi-scale approach allows for rapid process tuning and improves the predictive power of semiconductor process simulations. By combining first-principles insights, chamber-scale process inputs, and automated model calibration, ViennaPS provides a powerful and versatile framework for semiconductor topography evolution modeling. We demonstrate its capabilities through case studies, showcasing how this integrated approach improves process control, reduces reliance on empirical fitting, and accelerates technology development.

Dr. Lado Filipovic is an Associate Professor and Director of the Christian Doppler Laboratory for Multi-Scale Process Modeling at TU Wien’s Institute for Microelectronics in Vienna, Austria. He earned his PhD degree in Microelectronics from TU Wien and specializes in semiconductor sensor technology and process simulations. His research focuses on multi-scale process modeling, integrated sensors, and novel semiconductor materials, with an emphasis on equipment-informed inverse design and advanced semiconductor fabrication. Dr. Filipovic leads multiple research projects aimed at enhancing process simulations, improving device performance, and advancing sensor integration. His team has developed open-source TCAD tools, including ViennaPS, which is widely used for process and device modeling. A Senior Member of IEEE, he collaborates with leading industry partners and academic institutions worldwide to advance semiconductor processes, devices, and manufacturing technologies through improved modeling and simulation.

Superluminal lasers (SLLs) have garnered significant attention over the past decade due to their potential to revolutionize precision measurements and metrology. These lasers operate under conditions where the group velocity exceeds the speed of light in vacuum. In this regime, the laser's spectral sensitivity to changes in the ambient parameters becomes significantly higher than that of conventional lasers, making SLLs highly attractive for various sensing applications, including gravitational wave detection, dark matter search, and navigation-grade rotation sensing. In this talk, I will first show different approaches we have developed to realize the gain and dispersion properties for superluminal lasers based on non-linear interaction of light with hot Rb atoms. Next, I will present how the implementation of this interaction within an optical cavity, and inducing lasing under such unique conditions, enhances the spectral sensitivity of the laser to perturbation, making it a promising candidate for ultrasensitive optical sensing. Finally, I will introduce a novel concept for the superluminal laser cavity design incorporating polarization-selective reflectors, which enable efficient coupling of the interaction beams into the cavity while minimizing losses for the lasing mode.

Yael Sternfeld obtained her BSc and MSc degrees in Physics from Tel Aviv University. Since 2021, she has been working on her PhD under the supervision of Prof Jacob Scheuer and collaborating closely with Prof Selim Shahriar from Northwestern University. Yael has been awarded the prestigious Rothschild fellowship for post-doctoral studies. She also received an excellent research prize from the physics department at Tel Aviv University, and she won the Electro-Optics fund competition of the physical electronic department for best master's thesis.