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Yuan Y, Patel RK, Banik S, Reta TB, Bisht RS, Fong DD, Sankaranarayanan SKRS, Ramanathan S. Proton Conducting Neuromorphic Materials and Devices. Chem Rev 2024. [PMID: 39038231 DOI: 10.1021/acs.chemrev.4c00071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.
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Affiliation(s)
- Yifan Yuan
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Ranjan Kumar Patel
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Suvo Banik
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Tadesse Billo Reta
- Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Ravindra Singh Bisht
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Dillon D Fong
- Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Subramanian K R S Sankaranarayanan
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Shriram Ramanathan
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
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2
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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Park TJ, Deng S, Manna S, Islam ANMN, Yu H, Yuan Y, Fong DD, Chubykin AA, Sengupta A, Sankaranarayanan SKRS, Ramanathan S. Complex Oxides for Brain-Inspired Computing: A Review. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2203352. [PMID: 35723973 DOI: 10.1002/adma.202203352] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/02/2022] [Indexed: 06/15/2023]
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.
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Affiliation(s)
- Tae Joon Park
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Sunbin Deng
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Sukriti Manna
- Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, 60439, USA
| | - A N M Nafiul Islam
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Haoming Yu
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Yifan Yuan
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Dillon D Fong
- Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Alexander A Chubykin
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47907, USA
| | - Abhronil Sengupta
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Subramanian K R S Sankaranarayanan
- 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
| | - Shriram Ramanathan
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
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Bisht RS, Park J, Yu H, Wu C, Tilak N, Rangan S, Park TJ, Yuan Y, Das S, Goteti U, Yi HT, Hijazi H, Al-Mahboob A, Sadowski JT, Zhou H, Oh S, Andrei EY, Allen MT, Kuzum D, Frano A, Dynes RC, Ramanathan S. Spatial Interactions in Hydrogenated Perovskite Nickelate Synaptic Networks. NANO LETTERS 2023; 23:7166-7173. [PMID: 37506183 DOI: 10.1021/acs.nanolett.3c02076] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
A key aspect of how the brain learns and enables decision-making processes is through synaptic interactions. Electrical transmission and communication in a network of synapses are modulated by extracellular fields generated by ionic chemical gradients. Emulating such spatial interactions in synthetic networks can be of potential use for neuromorphic learning and the hardware implementation of artificial intelligence. Here, we demonstrate that in a network of hydrogen-doped perovskite nickelate devices, electric bias across a single junction can tune the coupling strength between the neighboring cells. Electrical transport measurements and spatially resolved diffraction and nanoprobe X-ray and scanning microwave impedance spectroscopic studies suggest that graded proton distribution in the inhomogeneous medium of hydrogen-doped nickelate film enables this behavior. We further demonstrate signal integration through the coupling of various junctions.
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Affiliation(s)
- Ravindra Singh Bisht
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Jaeseoung Park
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Haoming Yu
- School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Chen Wu
- Department of Physics, University of California, San Diego, La Jolla, California 92093, United States
| | - Nikhil Tilak
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Sylvie Rangan
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Tae J Park
- School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Yifan Yuan
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Sarmistha Das
- Department of Physics, University of California, San Diego, La Jolla, California 92093, United States
| | - Uday Goteti
- Department of Physics, University of California, San Diego, La Jolla, California 92093, United States
| | - Hee Taek Yi
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Hussein Hijazi
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Abdullah Al-Mahboob
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Jerzy T Sadowski
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Hua Zhou
- X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Seongshik Oh
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Eva Y Andrei
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Monica T Allen
- Department of Physics, University of California, San Diego, La Jolla, California 92093, United States
| | - Duygu Kuzum
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, California 92093, United States
| | - Alex Frano
- Department of Physics, University of California, San Diego, La Jolla, California 92093, United States
| | - Robert C Dynes
- Department of Physics, University of California, San Diego, La Jolla, California 92093, United States
| | - Shriram Ramanathan
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
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A distributed nanocluster based multi-agent evolutionary network. Nat Commun 2022; 13:4698. [PMID: 35948574 PMCID: PMC9365837 DOI: 10.1038/s41467-022-32497-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/02/2022] [Indexed: 11/25/2022] Open
Abstract
As an important approach of distributed artificial intelligence, multi-agent system provides an efficient way to solve large-scale computational problems through high-parallelism processing with nonlinear interactions between the agents. However, the huge capacity and complex distribution of the individual agents make it difficult for efficient hardware construction. Here, we propose and demonstrate a multi-agent hardware system that deploys distributed Ag nanoclusters as physical agents and their electrochemical dissolution, growth and evolution dynamics under electric field for high-parallelism exploration of the solution space. The collaboration and competition between the Ag nanoclusters allow information to be effectively expressed and processed, which therefore replaces cumbrous exhaustive operations with self-organization of Ag physical network based on the positive feedback of information interaction, leading to significantly reduced computational complexity. The proposed multi-agent network can be scaled up with parallel and serial integration structures, and demonstrates efficient solution of graph and optimization problems. An artificial potential field with superimposed attractive/repulsive components and varied ion velocity is realized, showing gradient descent route planning with self-adaptive obstacle avoidance. This multi-agent network is expected to serve as a physics-empowered parallel computing hardware. Designing an efficient multi-agent hardware system to solve large-scale computational problems through high-parallelism processing with nonlinear interactions remains a challenge. Here, the authors demonstrate that a multi-agent hardware system deploying distributed Ag nanoclusters as physical agents enables parallel, complex computing.
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Goteti US, Cai H, LeFebvre JC, Cybart SA, Dynes RC. Superconducting disordered neural networks for neuromorphic processing with fluxons. SCIENCE ADVANCES 2022; 8:eabn4485. [PMID: 35452286 PMCID: PMC9032950 DOI: 10.1126/sciadv.abn4485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 03/07/2022] [Indexed: 06/14/2023]
Abstract
In superconductors, magnetic fields are quantized into discrete fluxons (flux quanta Φ0), made of microscopic circulating supercurrents. We introduce a multiterminal synapse network comprising a disordered array of superconducting loops with Josephson junctions. The loops can trap fluxons defining memory, while the junctions allow their movement between loops. Dynamics of fluxons through such a disordered system through a complex reconfigurable energy landscape represents brain-like spiking information flow. In this work, we experimentally demonstrate a three-loop network using YBa2Cu3O7 - δ-based superconducting loops and Josephson junctions, which exhibit stable memory configurations of trapped flux in loops that determine the rate of flow of fluxons through synaptic connections. The memory states are, in turn, affected by the applied input signals but can also be externally configured electrically through control current/feedback terminals. These results establish a previously unexplored, biologically similar architectural approach to neuromorphic computing that is scalable while dissipating energy of atto Joules/spike.
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Affiliation(s)
- Uday S. Goteti
- Department of Physics, University of California, San Diego, CA 92093, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA
| | - Han Cai
- Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA
| | - Jay C. LeFebvre
- Department of Physics, University of California, Riverside, CA 92521, USA
| | - Shane A. Cybart
- Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA
| | - Robert C. Dynes
- Department of Physics, University of California, San Diego, CA 92093, USA
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Cheng R, Goteti US, Walker H, Krause KM, Oeding L, Hamilton MC. Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions. Front Neurosci 2021; 15:765883. [PMID: 34819835 PMCID: PMC8606638 DOI: 10.3389/fnins.2021.765883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/11/2021] [Indexed: 11/13/2022] Open
Abstract
We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirable properties such as inherent ultra-low energy per operation, high speed, dense integration, negligible loss, and natural spiking responses. In addition, they have a relatively straight-forward micro/nano fabrication, which shows promise for implementation of an enormous number of lossless interconnections that are required to realize complex neuromorphic systems. We simulate QPSJ-only, as well as hybrid QPSJ + JJ circuits for application in neuromorphic circuits including artificial synapses and neurons, as well as fan-in and fan-out circuits. We also design and simulate learning circuits, where a simplified spike timing dependent plasticity rule is realized to provide potential learning mechanisms. We also take an alternative approach, which shows potential to overcome some of the expected challenges of QPSJ-based neuromorphic circuits, via QPSJ-based charge islands coupled together to generate non-linear charge dynamics that result in a large number of programmable weights or non-volatile memory states. Notably, we show that these weights are a function of the timing and frequency of the input spiking signals and can be programmed using a small number of DC voltage bias signals, therefore exhibiting spike-timing and rate dependent plasticity, which are mechanisms to realize learning in neuromorphic circuits.
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Affiliation(s)
- Ran Cheng
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States
| | - Uday S Goteti
- Department of Physics, University of California, San Diego, San Diego, CA, United States
| | - Harrison Walker
- Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States.,Department of Materials Engineering, Auburn University, Auburn, AL, United States
| | - Keith M Krause
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States
| | - Luke Oeding
- Department of Mathematics and Statistics, Auburn University, Auburn, AL, United States
| | - Michael C Hamilton
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States
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