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Vijin Kizhake Veetil, Junyeob Song, Pradeep Namboodiri, Nikki Ebadollahi, Ashish Chanana, Aaron Katzenmeyer, Christian Pederson, Joshua Pomeroy, Jeff Chiles, Jeff Shainline, Kartik Srinivasan, Marcelo Davanco, Matthew Pelton
Color centers in silicon have recently gained considerable attention as a single-photon source [1,2] and as a spin qubit-photon interface [3] for quantum information applications. However, one of the major bottlenecks is their low overall brightness due to
While spiking neuromorphic hardware holds promise for efficient implementations of artificial intelligence, the impact has been limited due in part to a lack of learning algorithms that achieve performance superior to conventional deep learning. One
Bryce Primavera, Saeed Khan, Samuel Adler, Jeff Shainline
Superconducting optoelectronic hardware is promising for large-scale neuromorphic computing. In this work, analog circuits combining Josephson junctions and superconducting single-photon detectors are fabricated and shown to exhibit a variety of
Marla L. Dowell, Hannah Brown, Gretchen Greene, Paul D. Hale, Brian Hoskins, Sarah Hughes, Bob R. Keller, R Joseph Kline, June W. Lau, Jeff Shainline
The CHIPS and Science Act of 2022 called for NIST to "carry out a microelectronics research program to enable advances and breakthroughs....that will accelerate the underlying R&D for metrology of next-generation microelectronics and ensure the
Superconducting Optoelectronic Networks (SOENs) combine pho- tonics and superconductors to instantiate computing systems that approach the fundamental limits of information processing in terms of speed and scalability. Overcoming the engineering challenges
Saeed Khan, Bryce Primavera, Jeff Chiles, Adam McCaughan, Sonia Buckley, Alexander Tait, Adriana Lita, John Biesecker, Anna Fox, David Olaya, Richard Mirin, Sae Woo Nam, Jeff Shainline
Superconducting optoelectronic hardware is being explored as a path towards artificial spiking neural networks with unprecedented scales of complexity and computational ability. Such hardware combines integrated-photonic components for few-photon, light
Michael Schneider, Emily Toomey, Graham Rowlands, Jeff Shainline, Paul Tschirhart, Ken Segall
Neuromorphic computing is a broad eld that uses biological inspiration to address computing design. It is being pursued in many hardware technologies both novel and conventional. Here we discuss the use of superconductive electronics for neuromorphic
Superconducting electronic circuits have much to o er with regard to neuromorphic hardware. Superconducting quantum interference devices (SQUIDs) can serve as an active element to perform the thresholding operation of a neuron's soma. However, a SQUID has
Adam McCaughan, Alexander N. Tait, Sonia Buckley, Jeff Chiles, Jeff Shainline, Sae Woo Nam, Dylan M. Oh
Computer-aided design (CAD) has become a critical element in the creation of nanopatterned structures and devices. In particular, with the increased adoption of easy-to-learn programming languages like python, there has been a significant rise in the
Any large-scale neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuro- morphic platforms that
To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and
Sonia M. Buckley, Alexander N. Tait, Jeffrey T. Chiles, Adam N. McCaughan, Saeed Khan, Richard Mirin, Sae Woo Nam, Jeffrey M. Shainline
We show several techniques for using integrated-photonic waveguide structures to simultaneously characterize multiple waveguide-integrated superconducting-nanowire detectors with a single fiber input. We demonstrate structures for direct comparison of
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a
Fine tuning of the parameters defining the physics of our universe has been proposed to result from the natural selection of universes capable of prolific reproduction. This cosmic reproduction may occur through singularities, and it has been argued that
Alexander N. Tait, Sonia M. Buckley, Jeffrey M. Shainline, Adam N. McCaughan, Jeffrey T. Chiles, Sae Woo Nam, Richard P. Mirin
Superconducting optoelectronic networks could achieve scales unmatched in hardware-based neuromorphic computing. After summarizing recent progress in this area, we report new results in cryogenic silicon photonic light sources, components central to these