Data-driven Discovery for Robust Optimization of Semiconductor Nanowire Lasers
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912035850158080 |
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| author | Church, Stephen A Vitale, Francesco Gopakumar, Aswani Gagrani, Nikita Zhang, Yunyan Jiang, Nian Tan, Hark Hoe Jagadish, Chennupati Liu, Huiyun Joyce, Hannah Ronning, Carsten Parkinson, Patrick |
| author_facet | Church, Stephen A Vitale, Francesco Gopakumar, Aswani Gagrani, Nikita Zhang, Yunyan Jiang, Nian Tan, Hark Hoe Jagadish, Chennupati Liu, Huiyun Joyce, Hannah Ronning, Carsten Parkinson, Patrick |
| contents | Active wavelength-scale optoelectronic components are widely used in photonic integrated circuitry, however coherent sources of light -- namely optical lasers -- remain the most challenging component to integrate. Semiconductor nanowire lasers represent a flexible class of light source where each nanowire is both gain material and cavity; however, strong coupling between these properties and the performance leads to inhomogeneity across the population. While this has been studied and optimized for individual material systems, no architecture-wide insight is available. Here, nine nanowire laser material systems are studied and compared using 55,516 nanowire lasers to provide statistically robust insight into performance. These results demonstrate that, while it may be important to optimise internal quantum efficiency for certain materials, cavity effects are always critical. Our study provides a roadmap to optimize the performance of nanowire lasers made from any material: this can be achieved by ensuring a narrow spread of lengths and end-facet reflectivities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_12643 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven Discovery for Robust Optimization of Semiconductor Nanowire Lasers Church, Stephen A Vitale, Francesco Gopakumar, Aswani Gagrani, Nikita Zhang, Yunyan Jiang, Nian Tan, Hark Hoe Jagadish, Chennupati Liu, Huiyun Joyce, Hannah Ronning, Carsten Parkinson, Patrick Optics Materials Science Active wavelength-scale optoelectronic components are widely used in photonic integrated circuitry, however coherent sources of light -- namely optical lasers -- remain the most challenging component to integrate. Semiconductor nanowire lasers represent a flexible class of light source where each nanowire is both gain material and cavity; however, strong coupling between these properties and the performance leads to inhomogeneity across the population. While this has been studied and optimized for individual material systems, no architecture-wide insight is available. Here, nine nanowire laser material systems are studied and compared using 55,516 nanowire lasers to provide statistically robust insight into performance. These results demonstrate that, while it may be important to optimise internal quantum efficiency for certain materials, cavity effects are always critical. Our study provides a roadmap to optimize the performance of nanowire lasers made from any material: this can be achieved by ensuring a narrow spread of lengths and end-facet reflectivities. |
| title | Data-driven Discovery for Robust Optimization of Semiconductor Nanowire Lasers |
| topic | Optics Materials Science |
| url | https://arxiv.org/abs/2405.12643 |