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Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
npj 2D Materials and Applications ( IF 9.7 ) Pub Date : 2021-01-04 , DOI: 10.1038/s41699-020-00186-w
Kookjin Lee , Sangjin Nam , Hyunjin Ji , Junhee Choi , Jun-Eon Jin , Yeonsu Kim , Junhong Na , Min-Yeul Ryu , Young-Hoon Cho , Hyebin Lee , Jaewoo Lee , Min-Kyu Joo , Gyu-Tae Kim

Two-dimensional (2D) layered materials such as graphene, molybdenum disulfide (MoS2), tungsten disulfide (WSe2), and black phosphorus (BP) provide unique opportunities to identify the origin of current fluctuation, mainly arising from their large surface areas compared with those of their bulk counterparts. Among numerous material characterization techniques, nondestructive low-frequency (LF) noise measurement has received significant attention as an ideal tool to identify a dominant scattering origin such as imperfect crystallinity, phonon vibration, interlayer resistance, the Schottky barrier inhomogeneity, and traps and/or defects inside the materials and dielectrics. Despite the benefits of LF noise analysis, however, the large amount of time-resolved current data and the subsequent data fitting process required generally cause difficulty in interpreting LF noise data, thereby limiting its availability and feasibility, particularly for 2D layered van der Waals hetero-structures. Here, we present several model algorithms, which enables the classification of important device information such as the type of channel materials, gate dielectrics, contact metals, and the presence of chemical and electron beam doping using more than 100 LF noise data sets under 32 conditions. Furthermore, we provide insights about the device performance by quantifying the interface trap density and Coulomb scattering parameters. Consequently, the pre-processed 2D array of Mel-frequency cepstral coefficients, converted from the LF noise data of devices undergoing the test, leads to superior efficiency and accuracy compared with that of previous approaches.



中文翻译:

通过电荷波动特征化来表征二维纳米电子器件的多机器学习方法

二维(2D)层状材料,例如石墨烯,二硫化钼(MoS 2),二硫化钨(WSe 2),而黑磷(BP)提供了独特的机会来识别电流波动的起因,这主要是由于与大体积同类产品相比表面积较大。在众多材料表征技术中,无损低频(LF)噪声测量已成为引起人们关注的理想工具,可用来确定主要的散射起源,例如不完美的结晶度,声子振动,层间电阻,肖特基势垒不均匀性以及陷阱和/或材料和电介质内部的缺陷。尽管有低频噪声分析的好处,但是,大量时间分辨的当前数据和随后需要的数据拟合过程通常会导致难以解释低频噪声数据,从而限制了其可用性和可行性,特别是对于二维分层范德华异质结构。在这里,我们介绍了几种模型算法,这些算法可以在32种情况下使用100多个LF噪声数据集对重要的设备信息进行分类,例如通道材料的类型,栅极电介质,接触金属以及化学和电子束掺杂的存在。 。此外,我们通过量化界面陷阱密度和库仑散射参数来提供有关设备性能的见解。因此,与经过测试的设备相比,从经过测试的设备的LF噪声数据转换而来的梅尔频率倒谱系数预处理2D数组可带来更高的效率和准确性。使用32种条件下的100多个LF噪声数据集,可以对重要的设备信息进行分类,例如通道材料的类型,栅极电介质,接触金属以及化学和电子束掺杂的存在。此外,我们通过量化界面陷阱密度和库仑散射参数来提供有关设备性能的见解。因此,与经过测试的设备相比,从经过测试的设备的LF噪声数据转换而来的梅尔频率倒谱系数预处理2D数组可带来更高的效率和准确性。使用32种条件下的100多个LF噪声数据集,可以对重要的设备信息进行分类,例如通道材料的类型,栅极电介质,接触金属以及化学和电子束掺杂的存在。此外,我们通过量化界面陷阱密度和库仑散射参数来提供有关设备性能的见解。因此,与经过测试的设备相比,从经过测试的设备的LF噪声数据转换而来的Mel倒谱系数的2D预处理阵列可带来更高的效率和准确性。我们通过量化界面陷阱密度和库仑散射参数来提供有关设备性能的见解。因此,与经过测试的设备相比,从经过测试的设备的LF噪声数据转换而来的梅尔频率倒谱系数预处理2D数组可带来更高的效率和准确性。我们通过量化界面陷阱密度和库仑散射参数来提供有关设备性能的见解。因此,与经过测试的设备相比,从经过测试的设备的LF噪声数据转换而来的梅尔频率倒谱系数预处理2D数组可带来更高的效率和准确性。

更新日期:2021-01-04
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