Data for the publication "Statistical Reproducibility of Selective Area Grown InAs Nanowire Devices"

New approaches such as selective area growth, 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.The .zip file contains the AFM and electrical transport data that was used to support the findings in the associated publication.The AFM data is available in .asc format and can be opened with FOSS software such as Gwyddion or read via python.The transport data is arranged into python dictionaries for easy navigation. A jupyter notebook is included describing the dictionary structure and providing example code for accessing the data.

新型方法，如选择性区域生长，其中晶体生长受光刻技术控制，使得自下而上的半导体纳米材料能够集成到大规模的经典和量子纳米电子学中。这要求对各个组件之间的再现性进行评估和优化。我们对名义上相同的选择性区域生长的InAs纳米线阵列中的结构和电子统计再现性进行了量化。通过全面的原子力显微镜研究和透射电子显微镜，获取了结构参数的分布情况，并将其与使用低温多路复用电路针对256个单独的SAG纳米线场效应晶体管所测得的低温电学性质的概率分布进行了比较。通过连续热循环之间的相关性测量，可以区分表面杂质散射和固定结构性质对器件再现性的贡献。结果证实了SAG纳米材料的潜力，而对于量化统计指标的方法论，对于进一步优化再现性至关重要。.zip文件包含了用于支持相关出版物发现所使用的AFM和电传输数据。AFM数据以.asc格式提供，可使用Gwyddion等开源软件打开，或通过Python读取。传输数据被组织成Python字典，便于导航。还包括一个jupyter笔记本，描述了字典结构，并提供了访问数据的示例代码。