Statistical Reproducibility of Selective Area Grown InAs Nanowire Devices
New approaches such as selective area growth (SAG), where crystal growth is lithographically controlled, allow the integration of bottom-up grown semiconductor nanomaterials in large-scale classical and quantum nanoelectronics. This calls for assessment and optimization of the reproducibility betwee...
| Autores: | , , , , , , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/381260 |
| Acesso em linha: | http://hdl.handle.net/10261/381260 http://arxiv.org/abs/2401.05084v2 |
| Access Level: | acceso abierto |
| Palavra-chave: | Multiplexers Nanowires Reproducibility Selective area growth Semiconductors |
| Resumo: | New approaches such as selective area growth (SAG), where crystal growth is lithographically controlled, allow the integration of bottom-up grown semiconductor nanomaterials in large-scale classical and quantum nanoelectronics. This calls for assessment and optimization of the reproducibility between individual components. We quantify the structural and electronic statistical reproducibility within large arrays of nominally identical selective area growth InAs nanowires. The distribution of structural parameters is acquired through comprehensive atomic force microscopy studies and transmission electron microscopy. These are compared to the statistical distributions of the cryogenic electrical properties of 256 individual SAG nanowire field effect transistors addressed using cryogenic multiplexer circuits. Correlating measurements between successive thermal cycles allows distinguishing between the contributions of surface impurity scattering and fixed structural properties to device reproducibility. The results confirm the potential of SAG nanomaterials, and the methodologies for quantifying statistical metrics are essential for further optimization of reproducibility. |
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