Optical Control of Adaptive Nanoscale Domain Networks
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arXiv
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2024
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| author | Zajac, Marc Zhou, Tao Yang, Tiannan Das, Sujit Cao, Yue Guzelturk, Burak Stoica, Vladimir Cherukara, Mathew Freeland, John W. Gopalan, Venkatraman Ramesh, Ramamoorthy Martin, Lane W. Chen, Long-Qing Holt, Martin Hruszkewycz, Stephan Wen, Haidan |
| author_facet | Zajac, Marc Zhou, Tao Yang, Tiannan Das, Sujit Cao, Yue Guzelturk, Burak Stoica, Vladimir Cherukara, Mathew Freeland, John W. Gopalan, Venkatraman Ramesh, Ramamoorthy Martin, Lane W. Chen, Long-Qing Holt, Martin Hruszkewycz, Stephan Wen, Haidan |
| contents | Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO3)n/(SrTiO3)n superlattices was directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that were previously inaccessible. Their reconfiguration and competition were quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics were quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity were drastically altered by light, holding promise for light-programmable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for remote control of adaptive nanoscale domain networks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_17067 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optical Control of Adaptive Nanoscale Domain Networks Zajac, Marc Zhou, Tao Yang, Tiannan Das, Sujit Cao, Yue Guzelturk, Burak Stoica, Vladimir Cherukara, Mathew Freeland, John W. Gopalan, Venkatraman Ramesh, Ramamoorthy Martin, Lane W. Chen, Long-Qing Holt, Martin Hruszkewycz, Stephan Wen, Haidan Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks Applied Physics Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO3)n/(SrTiO3)n superlattices was directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that were previously inaccessible. Their reconfiguration and competition were quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics were quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity were drastically altered by light, holding promise for light-programmable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for remote control of adaptive nanoscale domain networks. |
| title | Optical Control of Adaptive Nanoscale Domain Networks |
| topic | Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks Applied Physics |
| url | https://arxiv.org/abs/2406.17067 |