# Design, protocolization, and validation of a modular capture architecture for distributed, synchronized light transduction via SPADs on WR networks in passive 2D imaging applications

- Institution: CERN
- Speaker: Unai Sainz
- Year: 2026
- Language: English
- Subjects: Art, architecture and design
- Source: [CERN](https://videos.cern.ch/record/3025804)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Integrating SPAD-based photon-counting capabilities with sub-nanosecond distributed synchronization provided by an Ethernet network managed through the White Rabbit protocol offers significant advantages. By merging these two technologies, we introduce a novel concept termed White Rabbit time-resolved SPAD (WR-TR-SPAD). In this regard, we aim to develop an infrastructure that operationalizes this concept. To this end, we are designing, at the HDL level, a modular and parameterizable capture architecture that timestamps photon detection events using the White Rabbit PTP Core (WRPC) time base in a distributed and synchronized manner. In this context, the modularity of the architecture refers to the capability to connect (for example, via FMC) to a board equipped with WR technology (specifically, the Cute-WR-A7 in our implementation) and to exchange captured data or perform configurations with other nodes in the network, in our case via EtherBone. Likewise, the parameterizability of the architecture denotes the adaptability of the system to various scenarios, such as accommodating different numbers or types of sensors. Although we have not yet identified a specific use case, ongoing investigations include potential applications in muography and quantum imaging, both in the context of passive 2D imaging.

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## Lectures

1. [Design, protocolization, and validation of a modular capture architecture for distributed, synchronized light transduction via SPADs on WR networks in passive 2D imaging applications](https://invisiblenetwork.ai/learn/cern/3025804/08207192ebe3), 12 min (CERN's player)