Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2023Probabilistic computing with voltage-controlled dynamics in magnetic tunnel junctions16citations
  • 2023Probabilistic computing with voltage-controlled dynamics in magnetic tunnel junctions16citations

Places of action

Chart of shared publication
Shao, Yixin
2 / 2 shared
Finocchio, Giovanni
2 / 14 shared
Davila, Noraica
2 / 2 shared
Katine, Jordan A.
2 / 3 shared
Duffee, Christian
2 / 2 shared
Lopez-Dominguez, Victor
1 / 1 shared
Khalili Amiri, Pedram
1 / 2 shared
Chart of publication period
2023

Co-Authors (by relevance)

  • Shao, Yixin
  • Finocchio, Giovanni
  • Davila, Noraica
  • Katine, Jordan A.
  • Duffee, Christian
  • Lopez-Dominguez, Victor
  • Khalili Amiri, Pedram
OrganizationsLocationPeople

article

Probabilistic computing with voltage-controlled dynamics in magnetic tunnel junctions

  • Raimondo, Eleonora
  • Shao, Yixin
  • Finocchio, Giovanni
  • Davila, Noraica
  • Katine, Jordan A.
  • Duffee, Christian
Abstract

<jats:title>Abstract</jats:title><jats:p>Probabilistic (p-) computing is a physics-based approach to addressing computational problems which are difficult to solve by conventional von Neumann computers. A key requirement for p-computing is the realization of fast, compact, and energy-efficient probabilistic bits. Stochastic magnetic tunnel junctions (MTJs) with low energy barriers, where the relative dwell time in each state is controlled by current, have been proposed as a candidate to implement p-bits. This approach presents challenges due to the need for precise control of a small energy barrier across large numbers of MTJs, and due to the need for an analog control signal. Here we demonstrate an alternative p-bit design based on perpendicular MTJs that uses the voltage-controlled magnetic anisotropy (VCMA) effect to create the random state of a p-bit on demand. The MTJs are stable (i.e. have large energy barriers) in the absence of voltage, and VCMA-induced dynamics are used to generate random numbers in less than 10 ns/bit. We then show a compact method of implementing p-bits by using VC-MTJs without a bias current. As a demonstration of the feasibility of the proposed p-bits and high quality of the generated random numbers, we solve up to 40 bit integer factorization problems using experimental bit-streams generated by VC-MTJs. Our proposal can impact the development of p-computers, both by supporting a fully spintronic implementation of a p-bit, and alternatively, by enabling true random number generation at low cost for ultralow-power and compact p-computers implemented in complementary metal-oxide semiconductor chips.</jats:p>

Topics
  • impedance spectroscopy
  • semiconductor
  • laser emission spectroscopy
  • random