Highly Reproducible and CMOS-compatible VO2-based Oscillators for Brain-inspired Computing
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909199625093120 |
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| author | Maher, Olivier Bernini, Roy Harnack, Nele Gotsmann, Bernd Sousa, Marilyne Bragaglia, Valeria Karg, Siegfried |
| author_facet | Maher, Olivier Bernini, Roy Harnack, Nele Gotsmann, Bernd Sousa, Marilyne Bragaglia, Valeria Karg, Siegfried |
| contents | With remarkable electrical and optical switching properties induced at low power and near room temperature (68C), vanadium dioxide (VO2) has sparked rising interest in unconventional computing among the phase-change materials research community. The scalability and the potential to compute beyond the von Neumann model make VO2 especially appealing for implementation in oscillating neural networks for artificial intelligence (AI) applications, to solve constraint satisfaction problems, and for pattern recognition. Its integration into large networks of oscillators on a Silicon platform still poses challenges associated with the stabilization in the correct oxidation state and the ability to fabricate a structure with predictable electrical behavior showing very low variability. In this work, the role played by the different annealing parameters applied by three methods (slow thermal annealing, flash annealing, and rapid thermal annealing), following the vanadium oxide atomic layer deposition (ALD), on the formation of VO2 grains is studied and an optimal substrate stack configuration that minimizes variability between devices is proposed. Material and electrical characterizations are performed on the different films and a step-by-step recipe to build reproducible VO2-based oscillators is presented, which is argued to be made possible thanks to the introduction of a hafnium oxide (HfO2) layer between the silicon substrate and the vanadium oxide layer. Up to seven nearly identical VO2-based devices are contacted simultaneously to create a network of oscillators, paving the way for large-scale implementation of VO2 oscillating neural networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02822 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Highly Reproducible and CMOS-compatible VO2-based Oscillators for Brain-inspired Computing Maher, Olivier Bernini, Roy Harnack, Nele Gotsmann, Bernd Sousa, Marilyne Bragaglia, Valeria Karg, Siegfried Applied Physics Emerging Technologies With remarkable electrical and optical switching properties induced at low power and near room temperature (68C), vanadium dioxide (VO2) has sparked rising interest in unconventional computing among the phase-change materials research community. The scalability and the potential to compute beyond the von Neumann model make VO2 especially appealing for implementation in oscillating neural networks for artificial intelligence (AI) applications, to solve constraint satisfaction problems, and for pattern recognition. Its integration into large networks of oscillators on a Silicon platform still poses challenges associated with the stabilization in the correct oxidation state and the ability to fabricate a structure with predictable electrical behavior showing very low variability. In this work, the role played by the different annealing parameters applied by three methods (slow thermal annealing, flash annealing, and rapid thermal annealing), following the vanadium oxide atomic layer deposition (ALD), on the formation of VO2 grains is studied and an optimal substrate stack configuration that minimizes variability between devices is proposed. Material and electrical characterizations are performed on the different films and a step-by-step recipe to build reproducible VO2-based oscillators is presented, which is argued to be made possible thanks to the introduction of a hafnium oxide (HfO2) layer between the silicon substrate and the vanadium oxide layer. Up to seven nearly identical VO2-based devices are contacted simultaneously to create a network of oscillators, paving the way for large-scale implementation of VO2 oscillating neural networks. |
| title | Highly Reproducible and CMOS-compatible VO2-based Oscillators for Brain-inspired Computing |
| topic | Applied Physics Emerging Technologies |
| url | https://arxiv.org/abs/2403.02822 |