Quantum materials for brain sciences and artificial intelligence

Ramanathan, Shriram

doi:10.1557/mrs.2018.147

Cited by 13 publications

(12 citation statements)

References 36 publications

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“…Note that in the spin-polarized case, where one has partially filled d orbitals (full occupation of majority spin channel and partially filled minority spin channel), the superexchange interaction between Co-O-Co with almost 180°, favors AFM ordering, according to Goodenough-Kanamori-Anderson rules. In turn this ordering implies lower Co-O orbital hybridization and longer Co-O bond length, consistent with a previous study on SrCoO 3 (ref. 37 ).…”

Section: Magnetic Transitionssupporting

confidence: 92%

“…We note that a similar non-volatile resistive switching process may also be triggered in VO 2 (ref. 46 ), a broadly studied TMO for neuromorphic applications 3,47 . However, in this case the Fermi level is moved to the conduction band 48 as soon as vacancies are formed, and no intermediate resistive states may be created.…”

Section: Discussionmentioning

confidence: 99%

“…In the last decades, several transition metal oxides (TMO) have been proposed as promising resistive switching materials 3,4 , i.e. systems showing tunable resistance states, induced by an external electrical bias.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the metal-to-insulator transition in La1−xSrxCoO3−δ and its applications for neuromorphic computing

Zhang

Galli

2020

npj Comput Mater

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Transition metal oxides that exhibit a metal-to-insulator transition (MIT) as a function of oxygen vacancy concentration are promising systems to realize energy-efficient platforms for neuromorphic computing. However, the current lack of understanding of the microscopic mechanism driving the MIT hinders the realization of effective and stable devices. Here we investigate defective cobaltites and we unravel the structural, electronic, and magnetic changes responsible for the MIT when oxygen vacancies are introduced in the material. We show that, contrary to accepted views, cooperative structural distortions instead of local bonding changes are responsible for the MIT, and we describe the subtle interdependence of structural and magnetic transitions. Finally, we present a model, based on first principles, to predict the required electric bias to drive the transition, showing good agreement with available measurements and providing a paradigm to establish design rules for low-energy cost devices.

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Section: Magnetic Transitionssupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Understanding the metal-to-insulator transition in La1−xSrxCoO3−δ and its applications for neuromorphic computing

Zhang

Galli

2020

npj Comput Mater

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“…These are promising because their electronic properties can be tuned efficiently and reversibly. For example, they have recently gained attention as a platform for devices that could enable neuromorphic computing, which offers a new level of computational efficiency by creating artificial systems that can emulate the operation of animal brains [1,2]. This requires development of hardware elements whose electrical resistance changes under external stimuli (e.g., voltage or light pulse), emulating synaptic memory links between neurons [3,4].…”

Section: Introductionmentioning

confidence: 99%

Proton distribution visualization in perovskite nickelate devices utilizing nanofocused X-rays

Zaluzhnyy,

Sprau,

Tran

et al. 2021

Preprint

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We use a 30-nm x-ray beam to study the spatially resolved properties of a SmNiO 3 -based nanodevice that is doped with protons. The x-ray absorption spectra supported by density-functional theory (DFT) simulations show partial reduction of nickel valence in the region with high proton concentration, which leads to the insulating behavior. Concurrently, x-ray diffraction reveals only a small lattice distortion in the doped regions. Together, our results directly show that the knob which proton doping modifies is the electronic valency, and not the crystal lattice. The studies are relevant to on-going efforts to disentangle structural and electronic effects across metal-insulator phase transitions in correlated oxides.

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“…These macroscopic collective responses emerge from microscopic quantum mechanical interactions. As a result, brain-inspired computing paradigms—known broadly as neuromorphic ( 4 , 5 )—based on these quantum materials are prominent in the goals of various research efforts to explore and hopefully spawn the next technological revolution ( 6 – 10 ).…”

mentioning

confidence: 99%

Low-temperature emergent neuromorphic networks with correlated oxide devices

Goteti

Zaluzhnyy

Ramanathan

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Neuromorphic computing—which aims to mimic the collective and emergent behavior of the brain’s neurons, synapses, axons, and dendrites—offers an intriguing, potentially disruptive solution to society’s ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare earth nickelates. Together, our results demonstrate a research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems.

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Quantum materials for brain sciences and artificial intelligence

Abstract: Abstract

Cited by 13 publications

References 36 publications

Understanding the metal-to-insulator transition in La1−xSrxCoO3−δ and its applications for neuromorphic computing

Understanding the metal-to-insulator transition in La1−xSrxCoO3−δ and its applications for neuromorphic computing

Proton distribution visualization in perovskite nickelate devices utilizing nanofocused X-rays

Low-temperature emergent neuromorphic networks with correlated oxide devices

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