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Artificial intelligence (AI), machine learning, and edge computing are driving unprecedented demand
for high-performance, energy-efficient computing hardware. However, electronic accelerators face critical
limitations in speed, scalability, and energy consumption as transistor scaling slows down. Neuromorphic
photonics — the use of photonic circuits and systems to emulate neural architectures — has emerged as a
compelling approach to overcome these barriers by harnessing the inherent parallelism, high bandwidth, and
ultrafast response of light.
To fully realize the potential of neuromorphic photonic architectures, breakthroughs are required across the
underlying silicon photonic platforms. Recent advances in silicon photonic devices (e.g., MEMS), heterogeneous
integration, and novel optical materials such as barium titanate (BTO) and two-dimensional materials (e.g.,
MoS₂, graphene) are paving the way toward this goal. These innovations are enabling compact, CMOS-compatible
photonic computing systems that can deliver high-speed, low-latency, and energy-efficient information processing
— driving next-generation capabilities in AI acceleration, signal processing, sensing, and LIDAR.
This workshop aims to bring together experts from academia, research institutes, and industry across the world
to explore current challenges, technological enablers, and future directions in neuromorphic photonics. It will
also serve as a platform to identify opportunities for deeper cooperation in the next generation of computing
and communication systems.
Key topics of this workshop include:
Technology enablers
Architectures and applications
Workshop Organizers
Speakers
| Session 1: Enabling Technologies | |
|---|---|
| Workshop organizers | Opening remark |
| Tiernan McCaughery (imec) | Heterogeneous integration approaches |
| Kyoungsik Yu (KAIST) | MEMS-based phase shifters and tunable couplers |
| Juerg Leuthold (ETH Zurich) | POH, Graphene-based components for high-speed photonic components |
| Donguk Nam (KAIST) | Materials for optical computing |
| Panel discussion (Next Gen. systems requirements, collaboration opportunities) | |
| Session 2: Architecture and Applications | |
| Apostolos Tsakyridis (Aristotle University of Thessaloniki) | Photonic transfer learning for next generation optical computing applications |
| Joonyoung Kim (imec) | Neuromorphic Enhanced Heterogeneous Integration for LiDAR |
| Jinhwa Gene (ETRI) | Free-space optical computing |
| Chaoran Huang (Chinese University of Hong Kong) | Hardware-aware integrated photonic neural networks |
| Leonardo Del Bino (Akhetonics) | Digital Photonic Computing |
| Panel discussion (Challenges for wide adoption, collaboration opportunities) | |
Hollow core fiber (HCF) technology has progressed from a laboratory curiosity to a strong candidate
for next-generation optical networks and advanced photonic systems. Recent breakthroughs in HCF designs have
achieved record-low losses (<0.1 dB/km), well below conventional solid-core silica fibers, while maintaining
ultra-low latency and greatly reduced nonlinear impairments. Combined with growing industrial investment and
early field deployments in cloud and data-center networks, these advances suggest that HCF is approaching a
tipping point towards wider commercial adoption.
At the same time, HCF are enabling transformative capabilities across multiple application domains. In
quantum technologies, they provide low-noise, low-latency channels for distributing quantum and classical
signals. In high-power and ultrafast photonics, HCFs supports kilowatt-class single-mode transmission at 1
µm and beyond. Gas-filled HCFs underpin the rapidly growing areas of “gas photonics”, enabling
long-interaction-length nonlinear optics and highly sensitive gas sensing and spectroscopy.
Despite this rapid progress, several key technological challenges remain: scalable, low-cost fabrication;
robust cabling and splicing; integration with conventional fiber infrastructure and photonic components; and
long-term reliability and standardization. This workshop will bring together leading experts from industry
and academia to:
Workshop Organizers
Speakers
| Session | |
|---|---|
| Workshop organizers | Welcome and Introduction |
| Peng Li (YOFC) | Recent advances in HCF fabrication |
| Robbie Mears (University of Bath) | HCFs for ultraviolet wavelengths |
| Yongmin Jung (ORC, University of Southampton) | HCF interconnection and device integration |
| Dawei Ge (China Mobile Research Institute) | Long-haul transmission and field trials using HCFs |
| Marcus J. Clark (University of Bristol) | Quantum information transfer over HCF |
| Rodrigo Amezcua Correa (University of Central Florida) | High-power HCF beam delivery and gas-filled fibres |
| Panel discussion (Future directions and roadmap for HCF technology) | |
As digital services continue to expand and user expectations for high-speed and highly reliable
connectivity increase, optical networks are required to evolve beyond conventional data transmission. Future
networks must be capable of perceiving their own operational conditions, interpreting network states, and
responding autonomously in real time.
This workshop aims to explore how AI can enable intelligent decision-making and automated control in optical
networks. By utilizing sensing data collected from the network, AI techniques can detect anomalous traffic
behavior, identify performance degradation, and anticipate potential failures. Technologies such as digital
twins, predictive analytics, and closed-loop control are discussed as key enablers for proactive network
operation, improved service quality, and reduced operational complexity.
This workshop also aims to investigate the role of optical sensing and signal monitoring in enhancing network
awareness. Fiber sensing, optical performance monitoring, and in-home service quality measurements provide
valuable insights into physical impairments, including signal loss, fiber bending, and component aging, as well
as end-user experience. AI-based models are essential for interpreting these heterogeneous sensing data,
enabling early fault detection, risk prediction, and more accurate assessment of service quality.
Experts from telecom operators, equipment manufacturers, research institutes, and universities will join this
workshop. Together, they will explore what kinds of sensing data, AI models, and operational methods are needed
for next-generation optical networks. The goal is to better understand how AI and sensing can make networks more
reliable, more efficient, and easier to manage—and to identify key research questions for moving the technology
forward.
Workshop Organizers
Speakers
| Session 1: AI | |
|---|---|
| HanHyub Lee (ETRI) | Opening remark |
| Cen Wang (KDDI Research) | How to combine DSP-based performance sensing into AI-based control and management system |
| Qiang ZHANG (Huawei) | AI4N：AI enhanced high speed coherent optical transmission |
| Chansung Park (ETRI) | AI-vPON: Towards a trustworthy sim-to-real AI agent for reliable end-to-end PON operations |
| Yuanqiu Luo (FutureWei) | AI use cases for broadband access and home networking |
| Zuqing Zhu (University of Science and Technology of China) | Adaptive TPE for Optically-Interconnected AI Clusters |
| Session 2: Sensing | |
| Takeo Sasai (NTT) | Optical Network Tomography Toward Self-Aware and Optimized Fiber Networks |
| Philip Ji (NEC Laboratories America) | AI-assisted fiber sensing for network health protection |
| Young Wuk Lee (H4tech) | Artificial Intelligence and Sensing in Optical Communication Infra |
| YAN Yaxi (The Hong Kong Polytechnic University) | Integrated optical fiber sensing and communication techniques for optical access networks. |
| Panel discussion (AI & Sensing) | |
Recent years have seen a rapid evolution in optical transmission capacity, striving toward 1.6 Tbps
and beyond for both IMDD and digital coherent technologies. As physical bandwidth becomes a precious resource,
industry and standardization efforts have shifted focus toward higher baud-rate operation as the primary lever
for capacity growth.
However, as symbol rates continue to climb, we face a critical intersection of fundamental and practical
bottlenecks. These include increased sensitivity to component bandwidth, implementation penalties, and signal
integrity degradation—all of which become significantly more pronounced at ultra-high baud rates. These
challenges complicate not only performance optimization but also the realization of scalable, robust, and
cost-effective optical interfaces.
This workshop provides a forum to explore the boundaries of baud-rate scaling. We will address key questions:
By bridging perspectives from device physics, DSP design, and system architecture, this workshop aims to define the roadmap for next-generation high-capacity optical transmission.
Workshop Organizers
Speakers
| Session 1: Technical Enablers and Bottlenecks for Ultra-High Baud-Rate Transmission | |
|---|---|
| Workshop Organizers | Opening remark |
| Di Che (Nokia Bell Labs) |
High speed DSPs for 1.6Tbps and beyond |
| Josuke Ozaki (NTT Innovative Devices) |
High speed InP optics for 1.6 Tbps and beyond |
| Baile Chen (ShanghaiTech University) |
High speed photodiodes for 1.6Tbps and beyond |
| Kazuhiko Naoe (Lumentum) |
High Speed EA-DFB Technologies for IMDD Applications |
| Session 2: Recent trends in Market and Standardization towards 1.6Tbps and beyond | |
| Maxim Kuschnerov (Huawei) |
Recent Trends of 1.6Tbps and Beyond in IEEE Standardizations |
| Walter Lee (OE Solutions) |
Market trends and requirements for 800G/1.6Tbps |
| Paul Brooks (VIAVI Solutions) |
Post 800GbE Ethernet - ramping for the Petabyte era |
| Panel discussion | |
In this workshop, we cover two of the most dynamic frontiers in integrated photonics: Co-packaged
Optics (CPO) and FMCW LiDAR chips.
In the first session, we address the ‘IO Wall’. As AI models grow exponentially, the energy and bandwidth
required to move data are becoming unsustainable. CPO technology is considered the most prominent solution for
tackling these challenges. While various approaches have demonstrated unique advantages, we will discuss whether
a consensus is emerging on standard technologies for light sources, modulators, fiber interfaces, interposer
platforms as well as explore future directions.
In the second session, we shift our focus from moving data to sensing depth. Various forms of mobility
applications demand sensing solutions that are more cost-effective, compact, and robust than today’s LiDAR
modules with mechanical scanning. These requirements can be met by integrating LiDAR building block components
into a single chip or chiplets. We have invited experts from industry and academia to discuss the advantages and
limitations of the latest technologies; topics ranging from the level of chip integration and beam steering
methods to parallelization for high frame rates and managing nonlinearity in source chirping.
.
Workshop Organizers
Speakers
| Session 1: LiDAR chip | |
|---|---|
| Il-Sug Chung (UNIST) | Opening remark |
| Ming Wu (UC Berkeley) | Solid State LiDAR with Silicon Photonics Focal Plane Array |
| Tobias Kippenberg (EPFL) | Ultra-Low Loss Silicon Nitride Photonic Integrated Circuits: From Kerr Combs, Low Noise Frequency Agile Lasers to On Chip Erbium Amplifiers for FMCW Amplifiers for FMCW |
| Jongpil Ra (Lambda innoVision) | OPLL-Driven Optical Frequency Chirp Linearization for Coherent FMCW LiDAR with Enhanced Linearity and Stability |
| Panel discussion (LiDAR chip) | |
| Session 2: CPO/LPO | |
| Woo Young Choi (Yonsei University) |
Opening remark |
| TBA (TSMC) | TBA |
| Shinji Matsuo (NTT) | Membrane III-V Photonic Devices on Si Photonics Platform for CPO |
| Jesus M. Cumana (Corning) | The Role of Glass Substrate in Integrated Optical and Electrical Connectivity for Advanced Packaging |
| Younghyun Kim (Hanyang University) |
SiN-on-Glass (SING) as a Photonic Packaging Substrate for Next-Generation CPO |
| Binhao Wang (China) | TBA |
| Panel discussion (CPO/LPO) | |