PROGRAM

Bio: Eric Guichard, Ph.D., has over 30 years of experience in the semiconductor industry. Dr. Guichard serves as Senior Vice President and General Manager of Silvaco’s TCAD division since November 2012 and served as Vice President of Applications from July 2008 to November 2012. From September 1995 to July 2008, Dr. Guichard served in various roles with Silvaco SA, formerly known as Silvaco Data Systems, one of the wholly owned subsidiaries of Silvaco Inc., including as a General Manager and Applications Engineer. Eric Guichard received a M.S. in materials science and a Ph.D. in semiconductor physics from Institute National Polytechnique de Grenoble, France.

Title: Driving Four Decades of TCAD Innovation: From Physical Simulation to AI-Powered Digital Twins

Abstract: Fueled by the highly paced electronics industry, today’s cutting-edge device designs regularly make use of intricately shaped, multi-material, and ultra-confined 3D heterostructures and exploit strain, size, and quantum effects. These translate to progressively more complex physical modeling and simulation efforts, which is further intensified when optimization workflows are needed, such as Design Technology Co-Optimization (DTCO). All of this puts further pressure on industrial development cycles and workforce demands. The goal must be the abstraction of complexity to allow for a wider user base to get fast responding access to the knowledge TCAD provides. This is provided by Fab Technology Co-OptimizationTM (FTCOTM) which allows for the AI-driven training of a Digital Twin (DT) model based on comprehensive process-to-circuit level TCAD simulation data complemented by Fab data. The DT model provides instantaneous feedback to non-TCAD experts who need to understand correlations between individual process parameters and specific device and/or circuit characteristics. This allows for on-the-spot decision making in the Fabs, aiding in optimizing process and design decisions, accelerating development cycles, and reducing workforce demands.
 

Bio: Zlatan Stanojević is the Chief Technology Officer (CTO) of Global TCAD Solutions (GTS), an independent European TCAD software vendor, where he supervises the company’s R&D activities. His research interests include process and device TCAD, semi-classical and quantum modeling of carrier transport effects in low-dimensional structures, Design-Technology Co-Optimization, as well as simulator design and numerical algorithms. He is the lead designer of the commercially successful GTS Nano Device Simulator (NDS), a Subband-Boltzmann-Transport-based device simulator. He has also co-supervised the development of the parasitics extraction engine PEX, part of GTS Cell Designer, and the GTS ProSim process simulation software.

Zlatan studied Microelectronics at the Vienna University of Technology where he received his MSc and PhD degrees in 2009 and 2016, respectively. He was member of the IEDM Modeling and Simulation subcommittee in 2021 and 2022 and its chair in 2023, and has been part of the ESSERC Track 3 TPC since 2019, which he is chairing in 2025.

Title: Is there anything left to do in TCAD?
 
Abstract: Over the past decade, the development of commercial Technology Computer-Aided Design (TCAD) software has followed an evolutionary rather than revolutionary path. Alongside established continuum and particle-based approaches in both process and device simulation, advanced carrier transport models – such as deterministic bulk and subband Boltzmann Transport Equation (BTE) solvers and Non-Equilibrium Green’s Functions (NEGF) – have been incorporated into the TCAD toolkit for single-device simulation. At the system level, the field of Design-Technology Co-Optimization (DTCO) has expanded to encompass variability, reliability, and the extension of TCAD methodologies from devices to circuits. However, most of these innovations were introduced over a decade ago, prompting the question: What remains to be developed in TCAD? This talk addresses this question by analyzing current limitations and potential future directions in TCAD across three key dimensions: (1) Fidelity, (2) Integration, and (3) Efficiency – each with particular relevance in commercial and industrial contexts. We examine ongoing challenges in classical TCAD, advanced transport modeling, and DTCO flows, and propose remedies supported by specific tools and examples. Among these remedies, we include various methodologies related to artificial intelligence, machine learning, and hardware accelerators, particularly within the Efficiency dimension.

 

Bio: Srinivas Raghvendra is Vice President of Engineering and leads the TCAD, Mask Solutions and Smart Manufacturing group at Synopsys. His group works on software solutions that help optimize the technology development and manufacturing process. Previously he has held senior R&D positions in the design implementation groups at Synopsys. His worked has spanned logic synthesis, physical design, low power synthesis, OPC/Lithography, TCAD, and Smart Manufacturing technologies. Srini has been involved with the IC design and EDA industry for over 30 years. He is the co-author of 2 patents in logic synthesis. He has delivered several invited talks at conferences, and participated in many industry panels.
Srini is a graduate of the Stanford Executive Program of the Graduate School of Business at Stanford University and has a BSEE, and a MS in Computer Science.
Title: Advances in Power Electronics Design Fueled by Hyperconvergence
Abstract: Power electronics is at an inflection point, with innovative power transistor design being introduced to serve a variety of applications like fast chargers for consumer devices, EV traction inverters and on-board chargers, LIDAR, data center power supplies, and integrated voltage regulators. While Si power transistors still comprise most of the market, SiC and GaN power transistors are increasingly being adopted in applications where the higher power capability of SiC and faster switching frequency of GaN offers compelling system benefits, while ultra-wide bandgap (UWBG) materials such as Ga2O3, AlN and diamond are being explored to offer further system level benefits. Power transistor design requires co-design of the active area unit cells, edge terminations and in-chip gate interconnects. Recent advances in meshing techniques, GPU acceleration of numerical algorithms, and physics models for Si, WBG and UWBG materials enable 3D TCAD design of edge terminations and electro-thermal layout optimization to minimize in-chip current and temperature non-uniformities. Traditionally, power transistor design has been addressed sequentially with multiple trial-and-error iterations. A hyperconvergence of design tools covering a wide range from ab-initio material engineering to 3D TCAD, electro-thermal analysis, and layout optimization of the power transistor is being introduced to address all these critical design aspects in an automated way.
 

Bio: Helene Wehbe-Alause has a PhD in Physics of semiconductors, on III-V quantum wells heterostructures for optical filtering, in collaboration with Schlumberger. After a few years in Thales avionics developing MEMS accelerometric and gyrometric sensors for space and aeronautics, she joined ST Microelectronics in 2000. Since 2011, she has been managing ST Process integration team for CIS : developing the BSI, 3D stacking brick, 90nm and 40nm SPAD technologies, and bringing in production CDTI based rolling and global shutters. In 2017 she also integrated the TCAD simulation team in her service in order that Simulation activities sustain the developments of new pixels from their conception until their full qualification. Since 2021, she has been appointed Director of the Technology for Optical Sensors Department which mission is supporting STM Imaging Division roadmap with developing differentiated technologies adapted to customer needs : provide innovating pixel architectures, from design to full validation of pixel performance, put in place and insure the quality and readiness for industrialisaton of the process flow for the technology.

Title: Advanced Optoelectronic Technologies : Device Optimization and Securing Production with Predictive Simulation Tool-chains

Abstract: The continuous evolution of semiconductor consumer electronic global market, including autonomous systems, augmented and virtual reality (AR/VR), and energy-efficient technologies, necessitates the development of advanced optical sensing solutions. These applications demand innovative optoelectronic devices, such as RGB and near-infrared (NIR) image sensors, but also time-of-flight optical sensors and multi-spectral ambient light sensing. Sequential 3D stacking, hybrid bonding and pixel size shrinkage has revolutionized CIS design, enabling differentiated pixel architectures that meet the demands of emerging applications and hold significant promise for the next generation of optical sensors. Metasurfaces, advanced optical filters and surface structuration technologies also further expands the capabilities of optical sensors, paving the way for high-resolution, energy efficient imaging and all-in-one sensing solutions. This paper presents a comprehensive overview of the recent state-of-the-art advancements in CMOS image sensors (CIS), emphasizing their technological breakthroughs, challenges, and future directions. We highlight the importance of predictive simulation tools to address these challenges and make the best integration of advanced CIS into complex systems. Advanced multi-physics simulation capabilities are required to model light propagation, carrier transport and material properties, but also for the integration of optical devices into larger system modules. Additionally, neural-network-based approaches are required to emulate and optimize large meta-surface optical systems, but also to make possible the optimization of new optical enablers such as meta-surface-based color routers.
 

Bio: Since 2022, Mathieu Luisier has been Full Professor of Computational Nanoelectronics at ETH Zurich, Switzerland. He graduated in electrical engineering in 2003 and received his PhD in 2007, both from ETH Zurich. During that time, he started the development of a state-of-the-art quantum transport simulator called OMEN. After a one-year post-doc at ETH, he joined in 2008 the Network for Computational Nanotechnology at Purdue University, USA, as a research assistant professor. In 2011 he returned to ETH Zurich to become Assistant and then Associate Professor. His current research interests focus on the modeling of nanoscale devices, such as advanced transistors based on classical semiconductors and 2-D materials, photo-detectors/emitters, and non-volatile resistive memory cells.

Title: Acceleration of atomistic NEGF: algorithms, parallelization, and machine learning

Abstract: The Non-equilibrium Green’s function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices, e.g., transistors, photo-diodes, or memory cells, in the ballistic limit of transport or in the presence of various scattering sources such as electron-phonon, electron-photon, or even electron-electron interactions. The inclusion of all these mechanisms has been first demonstrated in small systems, composed of a few atoms, before being scaled up to larger structures made of thousands of atoms. Also the accuracy of the models has kept improving, from empirical to fully ab-initio ones. NEGF is nowadays widely used in combination with density functional theory (DFT) to investigate the electronic, thermal, or optical characteristics of different types of nano-devices. These progresses have been enabled by the development of dedicated numerical algorithms and by the parallelization of the workload, first on CPUs, now on GPUs.

In this presentation, I will review key achievements that have allowed to push back the limit of DFT+NEGF solvers beyond toy examples and I will illustrate them with recent applications. Also, I will show how graph neural networks and machine-learning can be leveraged to speed up ab-initio device simulations.
 

Bio: 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. He earned his PhD in Microelectronics from TU Wien and specializes in semiconductor sensor technology and process simulations. His research focuses on integrated sensors, multi-scale process modeling, and novel semiconductor materials. He leads various projects on process simulations, equipment-informed inverse design, and novel material studies, working to advance semiconductor fabrication and device performance. His team has developed open-source TCAD tools like ViennaPS and ViennaEMC, widely used for process and device modeling. A Senior Member of IEEE, he collaborates with leading industry partners and academic institutions worldwide.

Title: From Atoms to Reactors: Multi-Scale Modeling for Semiconductor Fabrication

Abstract: Accurately predicting surface topography evolution during semiconductor processing is essential for advanced device manufacturing and Process/Design Technology Co-Optimization (PTCO/DTCO). PTCO bridges semiconductor process development and circuit design, ensuring that yield, performance, and manufacturability are optimized together. In this talk, we present multi-scale modeling approaches that span atomistic, feature, and reactor scales to enable predictive process design and optimization. At the atomic scale, first-principles methods such as DFT and molecular dynamics provide insight into fundamental surface reactions, including adsorption, desorption, and sputtering. These reactions define surface evolution models that are implemented at the feature scale using tools like ViennaPS, our open-source framework for simulating 3D topography evolution during etching and deposition. Chamber-scale plasma simulations provide spatially resolved ion and neutral flux distributions, ensuring that the surface evolution models are driven by realistic process conditions. To further improve model fidelity, experimental SEM/TEM images are used for automated calibration, allowing for the extraction and automated tuning of reaction rates and material-specific parameters. To improve efficiency, surrogate models of the plasma equipment can be integrated at the feature scale to capture reactor behavior without the cost of full-scale simulation. Finally, lithography-induced effects, such as non-uniform exposure and proximity effect corrections (PEC), are incorporated to account for variations in feature profiles introduced during patterning. This multi-scale approach allows for end-to-end simulation of semiconductor processes, supporting inverse design workflows and reducing reliance on costly experimental iteration. We demonstrate the capabilities required of today’s process simulation frameworks through case studies involving FinFETs, 3D NAND, and photonic structures, highlighting how cross-scale modeling improves accuracy, efficiency, and scalability in technology development.

 

Bio: Andreas Rosskopf studied Applied Mathematics with a focus on Numerical Simulation in Erlangen, Germany. Since 2012 he's with Fraunhofer IISB in Erlangen; in 2018 he founded the working group "AI-augmented Simulation “ combing AI and numerical approaches for the simulation and optimization of power electronic devices and systems. Since 2023 he's head of the "Modeling and Artificial Intelligence" department of the Fraunhofer IISB designing digital solutions in the field of power electronics, Technology Computer-Aided Design and lithography.

Title: Scientific Machine Learning (SciML) - How the fusion of AI and physics is giving rise to promising simulation methodologies.

Abstract: Established approaches to simulating semiconductor processes and devices typically solve physically motivated equations or use Neural Networks (NN) trained with large amounts of measurement data to approximate the real system or process. With scientific machine learning (SciML), a new methodology is emerging that combines both and opens up new possibilities in taylored, real-time simulation and optimization of complex physical and chemical systems. We present and examine different NN architectures and learning strategies that allow approximating solutions of PDEs. We show the potential of separating model generation and inferance for (topology-)optimization, explainability and real-time simulation and provide an outlook on the possibilities of this method in future engineering processes.
 

Vihar is a Professor of Nanoelectronics and the Leader of the DeepNano Group at the University of Glasgow. He has more than 15 years of experience in developing numerical solvers and machine learning methods that are used for modelling and simulations of various semiconductor devices, such as nanowire transistors, tunnelling FETs, biosensors, current transport in inorganic molecules, Josephson’s junctions, physical unclonable functions and molecular flash memories.

Vihar has participated in 12 European and UK projects in total. From 2018 until 2022 he held an EPSRC Industrial Fellowship called Quantum Simulator for Entangled Electronics. Since his appointment as a Lecturer in 2015, he has secured funding of around £1.5M as a PI and around £5.0M as a co-PI.
For more information, please see his University of Glasgow.

Title: Modelling and simulations of biosensors: from analytical to machine learning approaches

Abstract:  Biosensors can be broadly classified into different types based on the method used for signal transduction, including electrochemical, optical, thermal, piezoelectric and magnetic biosensors. Due to a vast area of possibilities, in this talk, I will focus my attention on electrochemical biosensors and I will discuss the underlying physical and electrical principles of operations and their areas of applications.
Using simulations and modelling is the most cost, time and resource effective method to test new devices and explain physical, chemical and electrical concepts. To explain the operation of such electrochemical biosensor devices, I will present different simulations methods and approaches. This talk will cover various simulation techniques covering analytical and numerical methods and how these approaches are used to train neural networks. I will discuss the advantages and disadvantages of each of these methods and summarize the current state of the art in modelling of biosensors. I will use practical examples and case study emphasizing how hybrid modelling strategies can bridge the gap between physics-based and data-driven models.

 

Abstract: Over the past decade, advances in artificial intelligence have led to the development of deep learning models to accelerate atomic-scale materials simulation. A major focus has been on deep learning interatomic potentials, which learn the potential energy surface (PES) from density functional theory simulations and enable large-scale molecular dynamics (MD) simulations. In this talk, I will review recent advances in graph neural network interatomic potentials, highlighting their ability to predict phonon properties and compute material properties—such as thermal conductivity—via MD simulations. I will also discuss the emergence of foundation models trained on PES datasets spanning nearly the entire periodic table, opening new avenues for materials discovery and design.
Short bio: Benoit Sklénard is a permanent researcher and leads the Advanced Simulation Group in the "Simulation and Modelling Laboratory" at CEA-Leti. He earned a Dipl.-Ing. degree in Electrical Engineering and a Master’s degree in Microelectronics from the National Institute of Applied Sciences (INSA) in Lyon in 2010. In 2014, he completed his PhD at the Grenoble Institute of Technology. His research interests include atomistic and mesoscale modeling and simulation of materials for nanoelectronics applications. Currently, his work focuses on combining atomistic simulation with artificial intelligence techniques to enhance the understanding and development of materials for advanced technologies.

Abstract: Scientific Machine Learning (SciML) is a currently active field of research aiming at leveraging Artificial Neural Networks (ANN) for scientific computing tasks in computational physics. It encompasses a wide variety of approaches and methodologies from purely model-driven methods to hybrid model-driven / data-driven methods. In this talk, I will first introduce the SciML domain by considering two popular ANN-based modeling frameworks, namely, PINN (Physics-Informed Neural Network) and DeepONet (Deep Operator Network). I will then focus on PINN-like approaches and review some achievements from various groups worldwide. I will then describe ongoing initiatives in our Inria group on the study of PINNs for time-domain and frequency-domain electromagnetics.
Short bio: Stéphane Lanteri is a senior research scientist at the Inria Research Center of University Côte d’Azur. He holds a PhD in Engineering Sciences from University of Nice-Sophia Antipolis (Year of defense: 1991) and a HDR (habilitation to advise doctoral theses) from the University of Nice/Sophia Antipolis (Year of defense: 2003 - Title: High-performance numerical methods on unstructured meshes with applications to compressible fluid dynamics). From May 1992 to October 1993, he was a postdoctoral fellow at the Center for Aerospace Structure, University of Colorado at Boulder, under the supervision of Charbel Farhat. His current research interests are concerned with numerical modeling of physical problems related to computational electromagnetics and computational nanophotonics: high order discontinuous Galerkin type approximation methods, reduced-order modeling methods, physics-based neural networks methods and parallel numerical algorithms for solving differential systems modeling electromagnetic wave interaction with complex media with a focus of nanoscale light-matter interaction. Since February 2020, he is the scientific head of the Atlantis project-team at the Inria Research Center of Université Côte d’Azur. He is also the scientific coordinator of the development of the DIOGENeS software suite, which is dedicated to computational nanophotonics. Stéphane Lanteri has authored or co-authored more that 80 publications in international journals in applied mathematics, scientific computing, computational physics and more recently, optics and photonics.Web: https://slanteri.github.io/.

Abstract: Effective thermal management is increasingly critical as rising power densities in emerging 3DIC systems drive higher temperatures and reduce reliability. With ICs vulnerable to nanoscale failures, multiscale thermal simulations are needed to help engineers identify and mitigate risks due to excess heat. Although thermal simulators exist at every length scale, they typically only operate at one; spanning multiple scales is too time- or resource-intensive. In this tutorial, we explore a new approach: an AI-accelerated atoms-to-circuit thermal simulation pipeline. We contrast it with traditional tools to demonstrate how AI overcomes existing bottlenecks. We focus on two parts: 1) the atoms-to-transistor portion, which uses neural networks to extend atomistic simulations to transistor scale, and 2) the transistor-to-circuit portion, which employs AI-enhanced physics models to scale up to fullchip or package simulations while preserving nanoscale geometric detail. Participants will gain an overview of the thermal simulation landscape and learn how AI is driving advancements.
Short bio: Alexander (Alex) Gabourie is a cofounder and the CTO of DeepSim, Inc., a Y Combinator-backed startup developing AI-accelerated physics simulation technology. At DeepSim, Alex focuses on multi-scale thermal-mechanical modeling to optimize the design and reliability of integrated circuits and packaging. He earned his Ph.D. and M.S. in EE at Stanford University, where he specialized in the thermal properties of semiconducting 2D materials. His research has been highlighted in Nature Electronics, selected as an Editor’s Pick in the Journal of Applied Sciences, and recognized with an IOP Publishing Top Cited Paper Award North America in the Nanosciences category.

Abstract: Analog in-memory computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In order to efficiently and accurately model key circuit and device behaviors of these systems, customized simulation frameworks can be used, which are seamlessly integrated with modernized deep learning frameworks. IBM released in 2021 the IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit, which simulates inference and training of DNNs using AIMC. More recently, a faster variant with less features that supports large-language models, AIHWKit-Lightning, has been released as well at https://github.com/IBM/aihwkit-lightning. In this workshop session, I will provide a deep dive into how inference and training can be performed using the AIHWKit and AIHWKit-lightning, and how users can expand and customize the toolkits for their own needs. Participants will be equipped with practical skills to model the training and inference of complex analog in-memory computing systems, using models developed from experimental data.
Short bio: Manuel Le Gallo joined IBM Research Europe in 2013, where he is currently employed as a Staff Research Scientist in the In-Memory Computing group of the Zurich laboratory. His main research interest is in using phase-change memory devices for non-von Neumann computing. He has co-authored more than 100 scientific papers in journal and conferences, holds 35 granted patents and has given 15 invited talks. He was appointed IBM Master Inventor in 2019 and 2024 for significant contributions to intellectual property and is a recipient of the MIT Technology Review's 2020 Innovators Under 35 award.

Abstract: Atomistic simulations have become increasingly important in modeling nanoscale devices. Most of the simulations are however limited to two-dimensional structures or molecule-sized systems comprising a few hundred atoms at most. For realistic three-dimensional simulations requiring extensive runs—such as those for industry-oriented calibration for drift-diffusion-based TCAD tools—it is essential to overcome the computational barrier by reducing simulation time from days or weeks to hours or less. We introduce the supercell Hamiltonian reduction method or supercell mode-space method (SuperMS) as a practical and scalable solution to this challenge. SuperMS eliminates the key limitations of traditional mode-space approaches—such as the inability to handle rapidly varying potentials, laterally heterogeneous device geometries, complex current paths, and multi-terminal structures. SuperMS is thus equivalent to the real-space method in all respects, while offering efficiency gains of several orders of magnitude and maintaining accuracy within a few percent. SuperMS is applicaple to both atomistic and non-atomistic Hamiltonians. Two approaches of Bloch-state-based and non-Bloch-state-based formulations are introduced, where the latter utilizes machine learning algorithms. We demonstrate SuperMS on a system comprising up to 2000 atoms, where extensive sampling is required to account for the inherent disorder—specifically, cation disorder in oxide semiconductor channel devices. We have also developed the core charge approximation (CCA) which provides a physical background for removing the ambiguity associated with the commonly-used effective charge approximation. CCA in combination with SuperMS provides an efficient and robust framework for handling of defect or interface states at atomistic level.
Short bio: Mincheol Shin received the Ph.D. degree in physics from Northwestern University, USA, in 1992. He was with Electronics and Telecommunications Research Institute, Korea, as a Senior Researcher from 1993 to 2002. In 2002, he joined the faculty of School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Korea, where he is currently a full professor. His research interest has focused on developing in-house simulation tools for nanoelectronic devices, specializing in advanced electronic transport calculations utilizing quantum mechanical methodologies.

Abstract: Magnetoresistive random access memory (MRAM) is an emerging nonvolatile memory with (sub)nanosecond switching times, high endurance, and long retention. In spin-transfer torque (STT) MRAM, the magnetization of the ferromagnetic (FM) free layer (FL) is inverted by utilizing the spin polarization of the charge current passing through the cell. Spin-orbit torque (SOT) MRAM is operated, however, by spin currents generated through spin-orbit coupling in an adjacent to the FL heavy metal (HM) layer or at the interface.
Modeling advanced magnetoresistive memory requires an accurate evaluation of spin currents and torques in magnetic tunnel junctions with composite free layers and several reference layers. We developed a fully three-dimensional finite element method (FEM) based modeling and simulation environment, which includes all physical phenomena responsible for proper MRAM operation. The magnetization dynamics is described by the Landau-Lifshitz-Gilbert equation, solved numerically. The effective field includes magnetic anisotropy, exchange, and demagnetization field contributions. The demagnetization field is calculated at disconnected magnetic domains by a hybrid boundary element/finite element method.
By coupling nonequilibrium spin and charge transport with magnetization dynamics, we accurately evaluate nonequilibrium spin accumulation and the corresponding torques acting on the magnetization. We confirm the versatility of our approach by applying it to multilayer STT-MRAM and demonstrating ultra-fast sub-nanosecond operation. We show that SOT-MRAM cells utilizing the magnetic spin Hall effect in a Mn3Sn layer, as well as cells based on a FM/HM/FM trilayer, can achieve deterministic sub-ns magnetic field-free switching. We also demonstrate that noncolinear antiferromagnet Mn3Sn is promising for emerging devices as a free switching layer. Electric magnetization control in a Mn3Sn/Mo/CoFeB trilayer structure opens exciting perspectives for creating innovative SOT-driven devices.
Short bio: Viktor Sverdlov obtained his Master of Science and PhD degrees in physics from the State University of St. Petersburg, Russia, in 1985 and 1989, respectively. From 1989 to 1999, he worked as a senior research scientist at the V.A. Fock Institute of Physics at the same university. Throughout his career, he has visited several notable institutions, including the ICTP in Italy (1993), the University of Geneva in Switzerland (1993-1994), the University of Oulu in Finland (1995), the Helsinki University of Technology in Finland (1996, 1998), the Free University of Berlin in Germany (1997), and NORDITA in Denmark (1998). In 1999, he became a staff research scientist at the State University of New York at Stony Brook. In 2004, he joined the Institute for Microelectronics at Technische Universität Wien, where he is currently an associate professor and the director of the Christian Doppler Laboratory for Nonvolatile Magnetoresistive Memory and Logic. His scientific interests include device simulations, computational physics, solid-state physics, and nanoelectronics.