Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applications
This paper demonstrated the fabrication, characterization, data-driven modeling, and practical application of a 1D SnO2 nanofiber-based memristor, in which a 1D SnO2 active layer was sandwiched between silver (Ag) and aluminum (Al) electrodes. This device yielded a very high ROFF : RON of ∼104 (ION...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Chip |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2709472323000382 |
| _version_ | 1827368212399915008 |
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| author | Soumi Saha Madadi Chetan Kodand Reddy Tati Sai Nikhil Kaushik Burugupally Sanghamitra DebRoy Akshay Salimath Venkat Mattela Surya Shankar Dan Parikshit Sahatiya |
| author_facet | Soumi Saha Madadi Chetan Kodand Reddy Tati Sai Nikhil Kaushik Burugupally Sanghamitra DebRoy Akshay Salimath Venkat Mattela Surya Shankar Dan Parikshit Sahatiya |
| author_sort | Soumi Saha |
| collection | DOAJ |
| description | This paper demonstrated the fabrication, characterization, data-driven modeling, and practical application of a 1D SnO2 nanofiber-based memristor, in which a 1D SnO2 active layer was sandwiched between silver (Ag) and aluminum (Al) electrodes. This device yielded a very high ROFF : RON of ∼104 (ION : IOFF of ∼105) with an excellent activation slope of 10 mV/dec, low set voltage of VSET ∼ 1.14 V and good repeatability. This paper physically explained the conduction mechanism in the layered SnO2 nanofiber-based memristor. The conductive network was composed of nanofibers that play a vital role in the memristive action, since more conductive paths could facilitate the hopping of electron carriers. Energy band structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claims reported in this paper. An machine learning (ML)–assisted, data-driven model of the fabricated memristor was also developed employing different popular algorithms such as polynomial regression, support vector regression, k nearest neighbors, and artificial neural network (ANN) to model the data of the fabricated device. We have proposed two types of ANN models (type I and type II) algorithms, illustrated with a detailed flowchart, to model the fabricated memristor. Benchmarking with standard ML techniques shows that the type II ANN algorithm provides the best mean absolute percentage error of 0.0175 with a 98% R2 score. The proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adopting the same fabrication recipe, which gave satisfactory predictions. Lastly, the ANN type II model was applied to design and implement simple AND & OR logic functionalities adopting the fabricated memristors with expected, near-ideal characteristics. |
| first_indexed | 2024-03-08T09:28:17Z |
| format | Article |
| id | doaj.art-cf85e51ab95a4a38815caca28e88513c |
| institution | Directory Open Access Journal |
| issn | 2709-4723 |
| language | English |
| last_indexed | 2024-03-08T09:28:17Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Chip |
| spelling | doaj.art-cf85e51ab95a4a38815caca28e88513c2024-01-31T05:45:53ZengElsevierChip2709-47232023-12-0124100075Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applicationsSoumi Saha0Madadi Chetan Kodand Reddy1Tati Sai Nikhil2Kaushik Burugupally3Sanghamitra DebRoy4Akshay Salimath5Venkat Mattela6Surya Shankar Dan7Parikshit Sahatiya8Department of Electrical & Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, IndiaDepartment of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, IndiaDepartment of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, IndiaDepartment of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, IndiaNanomagnetic Division, Ceremorphic India Pvt Ltd, IndiaNanomagnetic Division, Ceremorphic India Pvt Ltd, IndiaCeremorphic, Inc., San Jose, CA 95131, USADepartment of Electrical & Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, IndiaDepartment of Electrical & Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, India; Corresponding author.This paper demonstrated the fabrication, characterization, data-driven modeling, and practical application of a 1D SnO2 nanofiber-based memristor, in which a 1D SnO2 active layer was sandwiched between silver (Ag) and aluminum (Al) electrodes. This device yielded a very high ROFF : RON of ∼104 (ION : IOFF of ∼105) with an excellent activation slope of 10 mV/dec, low set voltage of VSET ∼ 1.14 V and good repeatability. This paper physically explained the conduction mechanism in the layered SnO2 nanofiber-based memristor. The conductive network was composed of nanofibers that play a vital role in the memristive action, since more conductive paths could facilitate the hopping of electron carriers. Energy band structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claims reported in this paper. An machine learning (ML)–assisted, data-driven model of the fabricated memristor was also developed employing different popular algorithms such as polynomial regression, support vector regression, k nearest neighbors, and artificial neural network (ANN) to model the data of the fabricated device. We have proposed two types of ANN models (type I and type II) algorithms, illustrated with a detailed flowchart, to model the fabricated memristor. Benchmarking with standard ML techniques shows that the type II ANN algorithm provides the best mean absolute percentage error of 0.0175 with a 98% R2 score. The proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adopting the same fabrication recipe, which gave satisfactory predictions. Lastly, the ANN type II model was applied to design and implement simple AND & OR logic functionalities adopting the fabricated memristors with expected, near-ideal characteristics.http://www.sciencedirect.com/science/article/pii/S2709472323000382Nanofiber-based memristorsData-driven modelingArtificial neural network (ANN)SnO2 |
| spellingShingle | Soumi Saha Madadi Chetan Kodand Reddy Tati Sai Nikhil Kaushik Burugupally Sanghamitra DebRoy Akshay Salimath Venkat Mattela Surya Shankar Dan Parikshit Sahatiya Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applications Chip Nanofiber-based memristors Data-driven modeling Artificial neural network (ANN) SnO2 |
| title | Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applications |
| title_full | Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applications |
| title_fullStr | Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applications |
| title_full_unstemmed | Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applications |
| title_short | Experimental demonstration of SnO₂ nanofiber-based memristors and their data-driven modeling for nanoelectronic applications |
| title_sort | experimental demonstration of sno₂ nanofiber based memristors and their data driven modeling for nanoelectronic applications |
| topic | Nanofiber-based memristors Data-driven modeling Artificial neural network (ANN) SnO2 |
| url | http://www.sciencedirect.com/science/article/pii/S2709472323000382 |
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