Welcome to the Lightwave Research Laboratory!
The Lightwave Research Laboratory is involved with multiple research programs on optical interconnection networks for advanced computing systems, data centers, optical packet-switched routers, and nanophotonic networks-on-chip for chip multiprocessors. We are developing a new class of nanoscale photonic interconnect technologies that seamlessly move data from on-chip networks, across memory and large computing systems with extreme energy efficiency. These future platforms, driven by nanophotonic-enabled interconnectivity, and the enormous bandwidth advantage of dense wavelength division multiplexing, will fundamentally transform the computation-communications architecture, to create systems able to meet explosive information demands at all scales.
With the growing demand for photonics based technologies in data centers and high performance computing applications, the Lightwave Research Laboratory aims to be on the cutting edge of research while creating feasible and deployable solutions to tackle real challenges faced in industry.
Join Us!
We currently have openings for Postdoctoral Fellows, PhD candidates, and MS students in our group! Interested candidates should contact Professor Bergman at [email protected] with their resume/CV and cover letter. We look forward to hearing from you!
Recent Publications
On-Chip Programmable MZI-Based Fourier Synthesizer for Ultra-Broadband Kerr Comb Equalization
Reconfiguration-Aware Direct-Connect AI Cluster using Spatial-and-Wavelength-Selective Switching
Silicon photonic DWDM micro-resonator link initialization under fabrication variation
Foundry-enabled wafer-scale characterization and modeling of silicon photonic DWDM links
ACTINA: Adapting Circuit-Switching Techniques for AI Networking Architectures
Reconfigurable Silicon Photonic Transceiver for Space-Based Communication Nodes
Silicon Photonic Accelerated Memory Pooling for Efficient Compute Resource Allocation
Highly uniform thermally undercut silicon photonic devices in a 300 mm CMOS foundry process
Efficient and Compact Multimode Interior-Ridge Heater for DWDM Systems
Path Divertibility in a Spatial- and Wavelength-Selective Switching Fabric
Automated Routing of a Spatial-and-Wavelength Selective Switching Fabric
Photonic Analog-to-Digital Architecture for Accelerating Multiply-Accumulate Operations
Dispersion-Engineered Resonator-Based Interleaver Co-Designed with Kerr Comb Source
Recent News
Professor Bergman Quoted in Scientific American
Prof. Bergman was recently quoted in a Scientific American article discussing the explosive growth of the High-Bandwidth Memory (HBM) chip industry.
Michael Cullen Profiled by Columbia Engineering
PhD student Michael Cullen was featured in an article by Columbia Engineering, highlighting his work as a CUbiC scholar and development of multi-chip modules.
Professor Bergman Elected to the American Academy of Arts & Sciences
Prof. Bergman was elected to the 2026 class of the American Academy of Arts & Sciences in recognition of her achievements in engineering and scientific research.
Professor Bergman Interviewed by @HPCpodcast
Prof. Bergman was interviewed for a second time by insideHPS's @HPCpodcast to discuss the newest developments in silicon photonics and co-packaged optics.
Isabel Song Profiled by the Department of Electrical Engineering
PhD student Isabel Song was featured in an article by the Department highlighting her National Science Foundation (NSF) Graduate Research Fellowship and Wei Family Private Foundation Fellowship.
Lightwave Research Group Summer 2025 Graduates
Our group recently graduated three PhD students. Dr. James Robinson, Dr. Brian Wu, and Dr. Robert Parsons all successfully defended their theses this summer. Additionally, postdoctoral scientist Dr. Yuyang Wang has joined the faculty at the University of Connecticut as an Assistant Professor of Electrical Engineering. We look forward to seeing the great things they will do next!
We need a rethinking of the boundaries between communication and computation and to fundamentally reinvent how data moves across systems with minimal energy consumption.
