Kelvin Probe Force Microscopy Imaging of Plasticity in Hydrogenated Perovskite Nickelate Multilevel Neuromorphic Devices
Ion drift in nanoscale electronically inhomogeneous semiconductors is among the most important mechanisms being studied for designing neuromorphic computing hardware. However, nondestructive imaging of the ion drift in operando devices directly responsible for multiresistance states and synaptic mem...
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| Published in | ACS nano Vol. 19; no. 7; pp. 6815 - 6825 |
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| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Chemical Society
25.02.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1936-0851 1936-086X 1936-086X |
| DOI | 10.1021/acsnano.4c11567 |
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| Abstract | Ion drift in nanoscale electronically inhomogeneous semiconductors is among the most important mechanisms being studied for designing neuromorphic computing hardware. However, nondestructive imaging of the ion drift in operando devices directly responsible for multiresistance states and synaptic memory represents a formidable challenge. Here, we present Kelvin probe force microscopy imaging of hydrogen-doped perovskite nickelate device channels subject to high-speed electric field pulses to directly visualize proton distribution by monitoring surface potential changes spatially, which is also supported with finite element-based electric field distribution studies. First-principles calculations provide mechanistic insights into the origin of surface potential changes as a function of hydrogen donor doping that serves as the contrast mechanism. We demonstrate 128 (7-bit) nonvolatile conductance levels in such devices relevant to in-memory computing applications. The synaptic plasticity measurements are implemented in spiking neural networks and show promising results for classification (SciKit Learn’s Iris and Wine data sets) and control (OpenAI’s CartPole-v1 and BipedalWalker-v3) simulation tasks. |
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| AbstractList | Ion drift in nanoscale electronically inhomogeneous semiconductors
is among the most important mechanisms being studied for designing
neuromorphic computing hardware. However, nondestructive imaging of
the ion drift in operando devices directly responsible for multiresistance
states and synaptic memory represents a formidable challenge. Here,
we present Kelvin probe force microscopy imaging of hydrogen-doped
perovskite nickelate device channels subject to high-speed electric
field pulses to directly visualize proton distribution by monitoring
surface potential changes spatially, which is also supported with
finite element-based electric field distribution studies. First-principles
calculations provide mechanistic insights into the origin of surface
potential changes as a function of hydrogen donor doping that serves
as the contrast mechanism. We demonstrate 128 (7-bit) nonvolatile
conductance levels in such devices relevant to in-memory computing
applications. The synaptic plasticity measurements are implemented
in spiking neural networks and show promising results for classification
(SciKit Learn’s Iris and Wine data sets) and control (OpenAI’s
CartPole-v1 and BipedalWalker-v3) simulation tasks. Ion drift in nanoscale electronically inhomogeneous semiconductors is among the most important mechanisms being studied for designing neuromorphic computing hardware. However, nondestructive imaging of the ion drift in operando devices directly responsible for multiresistance states and synaptic memory represents a formidable challenge. Here, we present Kelvin probe force microscopy imaging of hydrogen-doped perovskite nickelate device channels subject to high-speed electric field pulses to directly visualize proton distribution by monitoring surface potential changes spatially, which is also supported with finite element-based electric field distribution studies. First-principles calculations provide mechanistic insights into the origin of surface potential changes as a function of hydrogen donor doping that serves as the contrast mechanism. We demonstrate 128 (7-bit) nonvolatile conductance levels in such devices relevant to in-memory computing applications. The synaptic plasticity measurements are implemented in spiking neural networks and show promising results for classification (SciKit Learn’s Iris and Wine data sets) and control (OpenAI’s CartPole-v1 and BipedalWalker-v3) simulation tasks. Ion drift in nanoscale electronically inhomogeneous semiconductors is among the most important mechanisms being studied for designing neuromorphic computing hardware. However, nondestructive imaging of the ion drift in operando devices directly responsible for multiresistance states and synaptic memory represents a formidable challenge. Here, we present Kelvin probe force microscopy imaging of hydrogen-doped perovskite nickelate device channels subject to high-speed electric field pulses to directly visualize proton distribution by monitoring surface potential changes spatially, which is also supported with finite element-based electric field distribution studies. First-principles calculations provide mechanistic insights into the origin of surface potential changes as a function of hydrogen donor doping that serves as the contrast mechanism. We demonstrate 128 (7-bit) nonvolatile conductance levels in such devices relevant to in-memory computing applications. The synaptic plasticity measurements are implemented in spiking neural networks and show promising results for classification (SciKit Learn's Iris and Wine data sets) and control (OpenAI's CartPole-v1 and BipedalWalker-v3) simulation tasks.Ion drift in nanoscale electronically inhomogeneous semiconductors is among the most important mechanisms being studied for designing neuromorphic computing hardware. However, nondestructive imaging of the ion drift in operando devices directly responsible for multiresistance states and synaptic memory represents a formidable challenge. Here, we present Kelvin probe force microscopy imaging of hydrogen-doped perovskite nickelate device channels subject to high-speed electric field pulses to directly visualize proton distribution by monitoring surface potential changes spatially, which is also supported with finite element-based electric field distribution studies. First-principles calculations provide mechanistic insights into the origin of surface potential changes as a function of hydrogen donor doping that serves as the contrast mechanism. We demonstrate 128 (7-bit) nonvolatile conductance levels in such devices relevant to in-memory computing applications. The synaptic plasticity measurements are implemented in spiking neural networks and show promising results for classification (SciKit Learn's Iris and Wine data sets) and control (OpenAI's CartPole-v1 and BipedalWalker-v3) simulation tasks. |
| Author | Kuzum, Duygu Manna, Sukriti Sankaranarayanan, Subramanian K. R. S. Patel, Karan Ramanathan, Shriram Schuman, Catherine Patel, Ranjan Kumar Bisht, Ravindra Singh Andrei, Eva Y. Dey, Tamal Lai, Xinyuan Shah, Shaan Zhou, Yue |
| AuthorAffiliation | Department of Electrical Engineering & Computer Science University of Tennessee, Knoxville Department of Electrical and Computer Engineering Center for Nanoscale Materials Argonne National Laboratory Department of Physics and Astronomy University of California Rutgers University University of Illinois Department of Mechanical and Industrial Engineering |
| AuthorAffiliation_xml | – name: Center for Nanoscale Materials – name: Department of Electrical Engineering & Computer Science – name: University of California – name: Department of Physics and Astronomy – name: University of Tennessee, Knoxville – name: Department of Electrical and Computer Engineering – name: Argonne National Laboratory – name: Department of Mechanical and Industrial Engineering – name: Rutgers University – name: University of Illinois |
| Author_xml | – sequence: 1 givenname: Tamal orcidid: 0000-0003-4862-8107 surname: Dey fullname: Dey, Tamal organization: Department of Electrical and Computer Engineering – sequence: 2 givenname: Xinyuan surname: Lai fullname: Lai, Xinyuan organization: Rutgers University – sequence: 3 givenname: Sukriti surname: Manna fullname: Manna, Sukriti organization: University of Illinois – sequence: 4 givenname: Karan orcidid: 0000-0002-3653-6523 surname: Patel fullname: Patel, Karan organization: University of Tennessee, Knoxville – sequence: 5 givenname: Ranjan Kumar orcidid: 0000-0002-5778-4606 surname: Patel fullname: Patel, Ranjan Kumar organization: Department of Electrical and Computer Engineering – sequence: 6 givenname: Ravindra Singh orcidid: 0000-0002-8225-7306 surname: Bisht fullname: Bisht, Ravindra Singh organization: Department of Electrical and Computer Engineering – sequence: 7 givenname: Yue surname: Zhou fullname: Zhou, Yue organization: University of California – sequence: 8 givenname: Shaan surname: Shah fullname: Shah, Shaan organization: University of California – sequence: 9 givenname: Eva Y. orcidid: 0000-0002-2516-2749 surname: Andrei fullname: Andrei, Eva Y. organization: Rutgers University – sequence: 10 givenname: Subramanian K. R. S. surname: Sankaranarayanan fullname: Sankaranarayanan, Subramanian K. R. S. organization: University of Illinois – sequence: 11 givenname: Duygu surname: Kuzum fullname: Kuzum, Duygu organization: University of California – sequence: 12 givenname: Catherine surname: Schuman fullname: Schuman, Catherine organization: University of Tennessee, Knoxville – sequence: 13 givenname: Shriram orcidid: 0000-0002-6685-6798 surname: Ramanathan fullname: Ramanathan, Shriram email: shriram.ramanathan@rutgers.edu organization: Department of Electrical and Computer Engineering |
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| Keywords | nickelates memory Kelvin probe force microscopy neural networks synaptic plasticity imaging neuromorphic computing |
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| Title | Kelvin Probe Force Microscopy Imaging of Plasticity in Hydrogenated Perovskite Nickelate Multilevel Neuromorphic Devices |
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