R. M. Shelby

Engineering · Physical Sciences

61h-index17.1kcitations239works0.02yr avg
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Research Topics

Photonic and Optical Devices(41), Quantum optics and atomic interactions(41), Advanced Memory and Neural Computing(40), Photorefractive and Nonlinear Optics(37), Phase-change materials and chalcogenides(37)

Publications239 total

Robust quality factor assessment of high-coherence superconducting qubits
npj Quantum Information·2026Open Access
Quasiparticle tunneling as a probe of Josephson junction barrier and capacitor material in superconducting qubits
npj Quantum Information·2022· 28 citedOpen Access
Environmental radiation impact on lifetimes and quasiparticle tunneling rates of fixed-frequency transmon qubits
Applied Physics Letters·2022· 60 citedOpen Access
Merged-Element Transmons: Design and Qubit Performance
Physical Review Applied·2021· 49 citedOpen Access
Quasiparticle tunneling as a probe of Josephson junction quality and capacitormaterial in superconducting qubits
2021· 2 citedOpen Access
Design and Characterization of a Functional Merged Element Transmon
Bulletin of the American Physical Society·2021
Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges
2020· 4 cited
Detection of non-equilibrium quasiparticles in charge sensitive transmons
Bulletin of the American Physical Society·2020
Analog acceleration of deep learning using phase-change memory
Elsevier eBooks·2020
List of contributors
Elsevier eBooks·2020
NVMTS 2019 TOC
2019Open Access
Non-filamentary non-volatile memory elements as synapses in neuromorphic systems
2019Open Access
Analog memory-based techniques for accelerating the training of fully-connected deep neural networks (Conference Presentation)
2019· 1 cited
Inference of Long-Short Term Memory networks at software-equivalent accuracy using 2.5M analog Phase Change Memory devices
2019· 47 cited
Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance
Journal of Applied Physics·2018· 35 citedOpen Access
(Invited) Analog Memory Fully Connected Networks for Deep Neural Network Accelerated Training
ECS Meeting Abstracts·2018
Training fully connected networks with resistive memories: impact of device failures
Faraday Discussions·2018· 15 citedOpen Access
Recent progress in analog memory-based accelerators for deep learning
Journal of Physics D Applied Physics·2018· 218 citedOpen Access
Equivalent-accuracy accelerated neural-network training using analogue memory
Nature·2018· 1.1k citedOpen Access
Bidirectional Non-Filamentary RRAM as an Analog Neuromorphic Synapse, Part II: Impact of Al/Mo/Pr<sub>0.7</sub>Ca<sub>0.3</sub>MnO<sub>3</sub> Device Characteristics on Neural Network Training Accuracy
IEEE Journal of the Electron Devices Society·2017· 25 citedOpen Access
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Frequent Co-authors

Geoffrey W. Burr(14), R. M. Macfarlane(11), M. D. Levenson(8), Simone Raoux(7), Pritish Narayanan(6), Donald S. Bethune(5), Irem Boybat(4), Carmelo di Nolfo(4), Severin Sidler(4), Kumar Virwani(4), Bryan D. McCloskey(4), A. C. Luntz(4), H. Coufal(4), John A. Hoffnagle(4), C. Michael Jefferson(4), S. H. Perlmutter(4), D. F. Walls(4), Hyunsang Hwang(3), Charles Rettner(3), Martin Salinga(3)