Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme
The Gestalt principles of perceptual learning elucidate how the human brain categorizes and comprehends a set of visual elements grouped together. One of the principles of Gestalt perceptual learning is the law of closure which propounds that human perception has the proclivity to visualize a fragme...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-07-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200065 |
| _version_ | 1828403428862197760 |
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| author | Swapnadeep Poddar Zhesi Chen Zichao Ma Yuting Zhang Chak Lam Jonathan Chan Beitao Ren Qianpeng Zhang Daquan Zhang Guozhen Shen Haibo Zeng Zhiyong Fan |
| author_facet | Swapnadeep Poddar Zhesi Chen Zichao Ma Yuting Zhang Chak Lam Jonathan Chan Beitao Ren Qianpeng Zhang Daquan Zhang Guozhen Shen Haibo Zeng Zhiyong Fan |
| author_sort | Swapnadeep Poddar |
| collection | DOAJ |
| description | The Gestalt principles of perceptual learning elucidate how the human brain categorizes and comprehends a set of visual elements grouped together. One of the principles of Gestalt perceptual learning is the law of closure which propounds that human perception has the proclivity to visualize a fragmented object as a preknown whole by bridging the missing gaps. Herein, a letter recognition scheme emulating the Gestalt closure principle is demonstrated, utilizing artificial synapses made of 3D integrated MA3Bi2I9 (MBI) perovskite nanowire (NW) array. The artificial synapses exhibit short‐term plasticity (STP) and long‐term potentiation (LTP) and a transition from STP to LTP with increasing number of input electrical pulses. Initiatory ab initio molecular dynamics (AIMD) simulations attribute the conductance change in the MBI NW artificial synapses to the rotation of MA+ clusters, culminating in charge exchange between MA+ and Bi2I93−. Each device yields 40 conductance states with excellent retention >105 s, minimal variation (2σ/mean) <10%, and endurance of ≈105 cycles. MBI NW‐based artificial neural network (ANN) is constructed to recognize fragmented letters alike their distinction in unabridged form and also the gradual withering of synaptic connectivity with engendered missing fragments is demonstrated, thereby successfully implementing Gestalt closure principle. |
| first_indexed | 2024-12-10T10:19:00Z |
| format | Article |
| id | doaj.art-32a442ef0ed3404ea70a1587f130d37c |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-12-10T10:19:00Z |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-32a442ef0ed3404ea70a1587f130d37c2022-12-22T01:52:55ZengWileyAdvanced Intelligent Systems2640-45672022-07-0147n/an/a10.1002/aisy.202200065Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition SchemeSwapnadeep Poddar0Zhesi Chen1Zichao Ma2Yuting Zhang3Chak Lam Jonathan Chan4Beitao Ren5Qianpeng Zhang6Daquan Zhang7Guozhen Shen8Haibo Zeng9Zhiyong Fan10Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaDepartment of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaDepartment of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaDepartment of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaDepartment of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaDepartment of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaDepartment of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaDepartment of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaState Key Laboratory for Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences & Center of Materials Science and Optoelectronic Engineering University of Chinese Academy of Sciences Beijing 100083 ChinaMIIT Key Laboratory of Advanced Display Materials and Devices Institute of Optoelectronics & Nanomaterials School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing 210094 ChinaDepartment of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR ChinaThe Gestalt principles of perceptual learning elucidate how the human brain categorizes and comprehends a set of visual elements grouped together. One of the principles of Gestalt perceptual learning is the law of closure which propounds that human perception has the proclivity to visualize a fragmented object as a preknown whole by bridging the missing gaps. Herein, a letter recognition scheme emulating the Gestalt closure principle is demonstrated, utilizing artificial synapses made of 3D integrated MA3Bi2I9 (MBI) perovskite nanowire (NW) array. The artificial synapses exhibit short‐term plasticity (STP) and long‐term potentiation (LTP) and a transition from STP to LTP with increasing number of input electrical pulses. Initiatory ab initio molecular dynamics (AIMD) simulations attribute the conductance change in the MBI NW artificial synapses to the rotation of MA+ clusters, culminating in charge exchange between MA+ and Bi2I93−. Each device yields 40 conductance states with excellent retention >105 s, minimal variation (2σ/mean) <10%, and endurance of ≈105 cycles. MBI NW‐based artificial neural network (ANN) is constructed to recognize fragmented letters alike their distinction in unabridged form and also the gradual withering of synaptic connectivity with engendered missing fragments is demonstrated, thereby successfully implementing Gestalt closure principle.https://doi.org/10.1002/aisy.202200065artificial synapsesGestalt principle of closureletter recognitionperovskite nanowires |
| spellingShingle | Swapnadeep Poddar Zhesi Chen Zichao Ma Yuting Zhang Chak Lam Jonathan Chan Beitao Ren Qianpeng Zhang Daquan Zhang Guozhen Shen Haibo Zeng Zhiyong Fan Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme Advanced Intelligent Systems artificial synapses Gestalt principle of closure letter recognition perovskite nanowires |
| title | Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme |
| title_full | Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme |
| title_fullStr | Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme |
| title_full_unstemmed | Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme |
| title_short | Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme |
| title_sort | robust lead free perovskite nanowire array based artificial synapses exemplifying gestalt principle of closure via a letter recognition scheme |
| topic | artificial synapses Gestalt principle of closure letter recognition perovskite nanowires |
| url | https://doi.org/10.1002/aisy.202200065 |
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