Upcoming Talks

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.

Quantum information processing (QIP) relies on high-fidelity quantum state preparation and precise unitary operations; however, practical realizations face major challenges, as the permissible error per operation must remain below the fault-tolerant threshold, typically on the order of 10−3 to 10−2 in most quantum systems. Integrated photonic circuits are a leading platform for scalable QIP technologies, yet unavoidable fabrication imperfections and control inaccuracies often limit operational fidelities, preventing the realization of large-scale quantum circuits. Even small systematic errors can degrade fidelity below the fault-tolerant threshold. A powerful approach for mitigating such errors is the composite segmentation method, originally inspired by techniques from nuclear magnetic resonance, where operations are carefully divided into segments with tailored parameters to enhance robustness. However, traditional composite techniques rely on complex-valued parameter control, whereas integrated photonic systems inherently support only real-valued parameters, making direct application of these methods not just challenging but fundamentally infeasible without new design strategies. In this talk, I will present the detuning-modulated composite segmentation method recently developed in my lab, which enables robust quantum operations in integrated photonic platforms without requiring complex phase control. This approach corrects a wide range of errors and achieves fidelities surpassing the fault-tolerant threshold. I will share our recent numerical and experimental results demonstrating how detuning-modulated composite segmentation dramatically enhances error tolerance and achieves superior robustness in single-photon quantum gates, such as high-fidelity Hadamard gates, as well as in two-photon entangling gates within integrated quantum circuits. Our approach opens a new path toward scalable and fault-tolerant quantum integrated photonic technologies.

Haim Suchowski is a professor in the Department of Condensed Matter Physics at the School of Physics and Astronomy, Tel Aviv University. He completed his postdoctoral research at the University of California, Berkeley (2014) after earning his M.Sc. and Ph.D. at the Weizmann Institute of Science (2011). He holds a B.A. in Physics (2004) and a B.Sc. in Electrical Engineering (2004), both from Tel Aviv University. His research focuses on ultrafast dynamics in condensed matter systems and nanostructures, silicon photonics, and two-dimensional materials. His group also investigates quantum coherent control using ultrashort laser pulses, as well as novel control schemes in quantum integrated photonics and nonlinear optics. In addition to leading a vibrant research group, he actively collaborates with international partners at the interface of ultrafast photonics and quantum technologies. Prof. Suchowski holds more than 20 patents and is the co-founder of 3DOptix, a cloud-based optical simulation platform, as well as two stealth companies: Quantum Pulse, focused on robust integrated quantum photonics, and a second venture developing mid-infrared imaging in silicon via robust nonlinear upconversion. He is the recipient of several awards, including the Fulbright Postdoctoral Fellowship, the Alon Young Investigator Award, and an ERC grant for his project "MIRAGE 20-15".

Effective tooling is essential for rapid innovation, yet traditional Electronic Design Automation (EDA) often hinders progress in emerging fields like photonics and quantum computing. Our extensive experience at leading institutions such as Google, PsiQuantum, HP, Juniper Networks, and Rockley Photonics revealed that commercial tools frequently fall short due to misaligned incentives. To address this gap, we developed open-source solutions, notably GDSFactory, which enable researchers and practitioners to directly participate in shaping the future of the tool. Though these tools served us well in our own day jobs, we came to the conclusion that an open-core business would be necessary to support these tools in the long term and make them a viable commercial alternative. This talk highlights our journey, key achievements, and future directions in advancing photonics and quantum technologies.

Troy is an experienced engineer in the field of design automation. He graduated MIT in 2010 and worked for 5 years at Samsung Electro-Mechanics on the simulation and optimization of novel fluid dynamic bearings, a topic on which he holds multiple patents. Since then, he has pivoted his focus to photonics. He has spent over 10 years streamlining the development cycle for photonics-based transceivers, wearable sensors, and more, at companies such as Aurrion, Juniper Networks, and Rockley Photonics. Recently he has founded DoPlayDo, Inc., which aims to accelerate the development of advanced integrated circuits using open source tools, such as GDSFactory. He is based in Fujieda, Japan.

Mechanical methods to modulate light are among the most effective approaches and have served as a standard since the founding of the field of optics. However, they typically require moving large optical elements, leading to inherently slow response times. Reducing the size of these mechanical components is the most direct route to increasing speed, yet while Micro-Electromechanical Systems (MEMS) have advanced significantly with modern nanofabrication techniques, they remain limited to modulation frequencies of only a few MHz. Acoustic waves, in contrast, are one of the fastest forms of mechanical modulation, but their small displacements have traditionally not been sufficient to induce significant changes in the optical properties. Here, we introduce a nanophotonic opto-mechanical resonator that confines light to the same length scale as typical GHz acoustic displacements. By constructing the resonator from mechanically compliant rubber materials and harnessing an optical plasmon mode, we deform the optical mode shape and energy with surface acoustic waves. This approach enables a substantial resonance shift—from approximately 700 nm to 600 nm—and modulations at frequencies approaching 1 GHz. Moreover, we demonstrate that the acoustic excitation can be sculpted to produce complex dynamic optical amplitudes and phases over the device surface, enabling beam steering and lensing functionalities without the need for moving large optics. These results represent a significant advance in acousto-optic modulation, offering a versatile platform for both ultrafast modulation and precise material manipulation at nanometer and nanosecond scales. By scaling optical cavities to dimensions compatible with GHz acoustic excitations, our technique paves the way for a new generation of high-speed optical metasurfaces.

Skyler Selvin is a PhD candidate in Electrical Engineering at Stanford University, advised by Professor Mark Brongersma. He received his BS in Electrical Engineering from UCLA and MS in Electrical Engineering from Stanford. Prior to beginning his doctoral studies, he worked at HRL Laboratories as a development engineer, creating mechanically modulated low-frequency transmitters using magnetics. He then spent time at Tsinghua University, where he contributed to the development of novel RF-based contrast methods for photoacoustic imaging systems. Skyler’s current research focuses on nanophotonics and dynamic photonic devices, leveraging MEMS, acoustics, and soft materials to achieve mechanical modulation. Beyond his core work in nanophotonics, he is dedicated to designing ultra-low-cost solar solutions and electronic systems for rural communities in sub-Saharan Africa.