Upcoming Talks

Fiber optical networks have been widely deployed at different scales to form the core infrastructure of today’s Internet backbone, telecommunication networks, and smart connected communities. These networks can deliver high bandwidth data services at deterministic low latency leveraging advanced techniques and hardware such as wavelength-division multiplexing (WDM) technique and reconfigurable add-drop multiplexer (ROADM) units. Accurate modeling and performance estimation, as well as autonomous adaption and reconfiguration of optical links are essential for optical system designs, particularly due to wavelength-dependent gain spectrum of optical amplifiers and fiber nonlinearity. Moreover, each fiber-optic cable can also serve as a high-resolution sensor since it is sensitive to different environmental effects (e.g., vibration and temperature) due to the linear and nonlinear light scattering. In this talk, I will first present the modeling of erbium-doped fiber amplifiers (EDFAs) using machine learning (ML) and its application in signal quality estimation in multi-span ROADM systems. I will then present our investigation on the coexistence of heterogeneous communication, fiber sensing, and radio-over-fiber signals that co-propagate on the same fiber, focusing on their impact on key performance metrics including bit error rate (BER) and sensing resolution. Lastly, I will highlight a series of measurements and field trials that support our research, conducted on the PAWR COSMOS platform in Manhattan, NYC, and the Duke BlueFrog testbed in the Research Triangle, NC.

Tingjun Chen received the Ph.D. degree in Electrical Engineering from Columbia University in 2020, and the B.Eng. degree in Electronic Engineering from Tsinghua University in 2014. Between 2020–2021 he was a Postdoctoral Associate at Yale University. Since Fall 2021 he has been with Duke University where he is an Assistant Professor in the Departments of Electrical & Computer Engineering and Computer Science (secondary appointment). His research interests are in the area of networking and communications with a specific focus on next-generation wireless, optical, mobile networks, as well as Internet-of-Things (IoT) systems. Tingjun received the IBM Academic Award, the Google Research Scholar Award, the Columbia Engineering Morton B. Friedman Memorial Prize for Excellence, the Columbia University Eli Jury Award, and the Facebook Fellowship. He is also a co-recipient of several paper awards, including the ACM CoNEXT’16 Best Paper Award, ECOC’23 Best Paper Award, and Top-Scored Papers from OFC’23 and OFC’24. His Ph.D. thesis received the ACM SIGMOBILE Dissertation Award Runner-up.

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.

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.