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RECONFIGURABLE SUPERCONDUTING AND PHOTONIC TECHNOLOGIES OF THE FUTURE

Source: EU Funding & Tenders Portal

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Computing with light using integrated optics has seen huge progress over the last 3-4 years in multiple fields such as neuromorphic computing, quantum computing and on-chip data storage. This has created a vast ecosystem that relies on high-speed reconfigurations of nanophotonic circuits (such as their use as synapses or routing applications) and ultrafast yet high-resolution, low-power photodetec

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The project "RECONFIGURABLE SUPERCONDUTING AND PHOTONIC TECHNOLOGIES OF THE FUTURE" is an infrastructure initiative in the Power Generation (CCGT), Advanced Electronics, Electric Vehicles (EVs), Automotive sector, located in N/A, Netherlands. Taiyo aggregates data from EU Funding & Tenders Portal, including information on sponsoring government bodies, EPCs, and contractors.

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Computing with light using integrated optics has seen huge progress over the last 3-4 years in multiple fields such as neuromorphic computing, quantum computing and on-chip data storage. This has created a vast ecosystem that relies on high-speed reconfigurations of nanophotonic circuits (such as their use as synapses or routing applications) and ultrafast yet high-resolution, low-power photodetection. Currently, it is impossible to combine all these functionalities into an integrated platform that fits onto a single chip. In RESPITE, by utilizing our newly invented superconducting Joule switches as neurons, multi-level phase change memory elements as synaptic weights, and superconducting single-photon detector arrays as retina we will demonstrate a novel platform which combines vision and cognition on a single chip. This new platform will allow in-sensor neuromorphic computing with unprecedented performance levels. The platform will have attoJoule switching power consumption, sub-nanosecond latency, and high compactness (3000 neurons and >100K synapses on <5 mm2). Unlike other superconducting neuromorphic technologies, our new platform will be scalable, easy to fabricate, and compatible with low-cost cryostats, high-Tc superconductors, quantum applications, and on-chip learning architectures – making it a game changer for a wide range of users and disciplines.

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