Complex Oxides for Brain‐Inspired Computing: A Review

Park, Tae Joon; Deng, Sunbin; Manna, Sukriti; Islam, A. N. M. Nafiul; Yu, Haoming; Yuan, Yifan; Fong, Dillon D.; Chubykin, Alexander A.; Sengupta, Abhronil; Sankaranarayanan, Subramanian K. R. S.; Ramanathan, Shriram

doi:10.1002/adma.202203352

Title: Complex Oxides for Brain‐Inspired Computing: A Review

Abstract

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

Authors:

Park, Tae Joon ^[1]; Deng, Sunbin ^[1]; Manna, Sukriti ^[2]; Islam, A. N. M. Nafiul ^[3]; Yu, Haoming ^[1]; Yuan, Yifan ^[1]; Fong, Dillon D. ^[4]; Chubykin, Alexander A. ^[5]; Sengupta, Abhronil ^[3]; Sankaranarayanan, Subramanian K. R. S. ^[6];

^[1]

School of Materials Engineering Purdue University West Lafayette IN 47907 USA
Center for Nanoscale Materials Argonne National Laboratory Argonne IL 60439 USA
Department of Electrical Engineering The Pennsylvania State University University Park PA 16802 USA
Materials Science Division Argonne National Laboratory Lemont IL 60439 USA
Department of Biological Sciences Purdue Institute for Integrative Neuroscience Purdue University West Lafayette IN 47907 USA
Center for Nanoscale Materials Argonne National Laboratory Argonne IL 60439 USA, Department of Mechanical and Industrial Engineering University of Illinois Chicago Chicago IL 60607 USA

Publication Date:: Wed Nov 30 00:00:00 EST 2022

Research Org.:: Pennsylvania State Univ., University Park, PA (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Org.:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); National Science Foundation (NSF); US Army Research Office (ARO)

OSTI Identifier:: 1900536

Alternate Identifier(s):: OSTI ID: 1961599; OSTI ID: 1995819; OSTI ID: 2337476

Grant/Contract Number:: DE‐AC02‐06CH11357; SC0021118; AC02-06CH11357; 1904081; W911NF1920237

Resource Type:: Published Article

Journal Name:: Advanced Materials

Additional Journal Information:: Journal Name: Advanced Materials Journal Volume: 35 Journal Issue: 37; Journal ID: ISSN 0935-9648

Publisher:: Wiley Blackwell (John Wiley & Sons)

Country of Publication:: Germany

Language:: English

Subject:: 36 MATERIALS SCIENCE

Citation Formats


                    Park, Tae Joon, Deng, Sunbin, Manna, Sukriti, Islam, A. N. M. Nafiul, Yu, Haoming, Yuan, Yifan, Fong, Dillon D., Chubykin, Alexander A., Sengupta, Abhronil, Sankaranarayanan, Subramanian K. R. S., and Ramanathan, Shriram. Complex Oxides for Brain‐Inspired Computing: A Review.  Germany: N. p., 2022. 
Web.  doi:10.1002/adma.202203352.

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                    Park, Tae Joon, Deng, Sunbin, Manna, Sukriti, Islam, A. N. M. Nafiul, Yu, Haoming, Yuan, Yifan, Fong, Dillon D., Chubykin, Alexander A., Sengupta, Abhronil, Sankaranarayanan, Subramanian K. R. S., & Ramanathan, Shriram. Complex Oxides for Brain‐Inspired Computing: A Review.  Germany.  https://doi.org/10.1002/adma.202203352

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                    Park, Tae Joon, Deng, Sunbin, Manna, Sukriti, Islam, A. N. M. Nafiul, Yu, Haoming, Yuan, Yifan, Fong, Dillon D., Chubykin, Alexander A., Sengupta, Abhronil, Sankaranarayanan, Subramanian K. R. S., and Ramanathan, Shriram. Wed .  
"Complex Oxides for Brain‐Inspired Computing: A Review".  Germany.  https://doi.org/10.1002/adma.202203352.

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@article{osti_1900536,

  title        = {Complex Oxides for Brain‐Inspired Computing: A Review},

  author       = {Park, Tae Joon and Deng, Sunbin and Manna, Sukriti and Islam, A. N. M. Nafiul and Yu, Haoming and Yuan, Yifan and Fong, Dillon D. and Chubykin, Alexander A. and Sengupta, Abhronil and Sankaranarayanan, Subramanian K. R. S. and Ramanathan, Shriram},

  abstractNote = {The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.},

  doi          = {10.1002/adma.202203352},

  journal      = {Advanced Materials},

  number       = 37,

  volume       = 35,

  place        = {Germany},

  year         = {Wed Nov 30 00:00:00 EST 2022},

  month        = {Wed Nov 30 00:00:00 EST 2022}

}

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Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges
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Synaptic Plasticity Forms and Functions
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Electronic structure ofNiO∕Ag(100)thin films fromDFT+Uand hybrid functional DFT approaches
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EqSpike: Spike-driven equilibrium propagation for neuromorphic implementations
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Relaxation dynamics of ionic liquid—VO ₂ interfaces and influence in electric double-layer transistors
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Ionic radii from scaled particle theory of the salt effect
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Dynamic Field Modulation of the Octahedral Framework in Metal Oxide Heterostructures
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A scalable neuristor built with Mott memristors
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Strongly correlated perovskite lithium ion shuttles
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Analog-to-Digital Conversion With Reconfigurable Function Mapping for Neural Networks Activation Function Acceleration
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Complexity in Strongly Correlated Electronic Systems
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Analog memristive synapse based on topotactic phase transition for high-performance neuromorphic computing and neural network pruning
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Crystal structure of a Na+-bound Na+,K+-ATPase preceding the E1P state
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Long-lasting depression of parallel fiber-Purkinje cell transmission induced by conjunctive stimulation of parallel fibers and climbing fibers in the cerebellar cortex
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Site-Selective Mott Transition in Rare-Earth-Element Nickelates
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Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics
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Nonvolatile Memory Materials for Neuromorphic Intelligent Machines
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Defect Dynamics in 2-D MoS ₂ Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy
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Rare-earth nickelates R NiO ₃ : thin films and heterostructures
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Activity-dependent structural plasticity
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Probing bulk electronic structure with hard X-ray angle-resolved photoemission
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Which Model to Use for Cortical Spiking Neurons?
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$DFT + DMFT$ study of spin-charge-lattice coupling in covalent ${LaCoO}_{3}$
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Interplay between breathing mode distortion and magnetic order in rare-earth nickelates $R {NiO}_{3}$ within $DFT + U$
journal, October 2017

Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade.
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Enhancement of Ferroelectric Curie Temperature in BaTiO ₃ Films via Strain-Induced Defect Dipole Alignment
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The GW Compendium: A Practical Guide to Theoretical Photoemission Spectroscopy
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Chemical Expansivity of Electrochemical Ceramics
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Direct imaging of structural changes induced by ionic liquid gating leading to engineered three-dimensional meso-structures
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Electric-field control of tri-state phase transformation with a selective dual-ion switch
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Progress and Benchmark of Spiking Neuron Devices and Circuits
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Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
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Localized and Itinerant States in Lanthanide Oxides United by $G W @ LDA + U$
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Predicting crystal structure by merging data mining with quantum mechanics
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Phenomenological models of synaptic plasticity based on spike timing
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The Hodgkin-Huxley theory of the action potential
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A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input
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Habituation: A history
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Depth-Resolved Composition and Electronic Structure of Buried Layers and Interfaces in a LaNiO3/SrTiO3 Superlattice from Soft- and Hard- X-ray Standing-Wave Angle-Resolved Photoemission
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Size of oxide vacancies in fluorite and perovskite structured oxides
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The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain
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Complete phase diagram of rare-earth nickelates from first-principles
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Structural, magnetic and electronic properties of perovskites (R = rare earth)
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Learning through ferroelectric domain dynamics in solid-state synapses
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Doping-driven Mott transition inLa1−xSrxTiO3via simultaneous electron and hole doping oft2gsubbands as predicted byLDA+DMFTcalculations
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A correlated nickelate synaptic transistor
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The GW approximation: content, successes and limitations: The GW approximation
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Mott-transition-based RRAM
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$G W$ calculations on post-transition-metal oxides
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Collapse of magnetic moment drives the Mott transition in MnO
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Thermodynamic limit for synthesis of metastable inorganic materials
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The high-throughput highway to computational materials design
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BLAST: bridging length/timescales via atomistic simulation toolkit
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First-principles calculations of the electronic structure and spectra of strongly correlated systems: the LDA + U method
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Energetics of the coupled electronic–structural transition in the rare-earth nickelates
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From Bird Song to Neurogenesis
journal, February 1989

First principles study on hydrogen doping induced metal-to-insulator transition in rare earth nickelates RNiO ₃ (R = Pr, Nd, Sm, Eu, Gd, Tb, Dy, Yb)
journal, January 2020

Spin-density functional theories and their $+ U$ and $+ J$ extensions: A comparative study of transition metals and transition metal oxides
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Theoretical Calculation of Absolute Radii of Atoms and Ions. Part 2. The Ionic Radii
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Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array
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Good semiconductor band gaps with a modified local-density approximation
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Size and shape of oxygen vacancies and protons in acceptor-doped barium zirconate
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The Basis of the Electron Theory of Metals, with Special Reference to the Transition Metals
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Chemical expansion of nonstoichiometric Pr0.1Ce0.9O2−δ: Correlation with defect equilibrium model
journal, November 2011