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Robert M. Shelby

Researcher at IBM

Publications -  188
Citations -  14740

Robert M. Shelby is an academic researcher from IBM. The author has contributed to research in topics: Optical fiber & Holographic data storage. The author has an hindex of 51, co-authored 187 publications receiving 13332 citations. Previous affiliations of Robert M. Shelby include University of Zaragoza & University of Queensland.

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Phase-change random access memory: a scalable technology

TL;DR: This work discusses the critical aspects that may affect the scaling of PCRAM, including materials properties, power consumption during programming and read operations, thermal cross-talk between memory cells, and failure mechanisms, and discusses experiments that directly address the scaling properties of the phase-change materials themselves.
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Solvents' Critical Role in Nonaqueous Lithium-Oxygen Battery Electrochemistry.

TL;DR: Coulometry has to be coupled with quantitative gas consumption and evolution data to properly characterize the rechargeability of Li-air batteries, and chemical and electrochemical electrolyte stability in the presence of lithium peroxide and its intermediates is essential to produce a truly reversible Li-O2 electrochemistry.
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Neuromorphic computing using non-volatile memory

TL;DR: The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.
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Holographic data storage

TL;DR: An overview of the research effort on volume holographic digital data storage is presented, highlighting new insights gained in the design and operation of working storage platforms, novel optical components and techniques, data coding and signal processing algorithms, systems tradeoffs, materials testing and tradeoff, and photon-gated storage materials.
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Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

TL;DR: Using 2 phase-change memory devices per synapse, a 3-layer perceptron network is trained on a subset of the MNIST database of handwritten digits using a backpropagation variant suitable for NVM+selector crossbar arrays, obtaining a training (generalization) accuracy of 82.2%.