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Integration of electrochemical capacitance–voltage characteristics: a new procedure for obtaining free charge carrier depth distribution profiles with high resolution
Journal of Solid State Electrochemistry ( IF 2.5 ) Pub Date : 2020-11-05 , DOI: 10.1007/s10008-020-04855-0
George Yakovlev , Vasily Zubkov

A novel electrochemical profiling technique consisting in integration capacitance–voltage characteristics was proposed, developed and proven. The aim is to overcome the insufficient resolution in charge carrier concentration measurements during commonly used electrochemical capacitance–voltage profiling. This is very actual task for modern low-dimensional optoelectronics. According to the introduced “profiling window”, after each etching step the coordinate interval is selected, the concentration distribution in which is to be matched itself and by the first derivative at the edges with the concentration in adjacent intervals. To achieve these conditions the minimum dissipation factor should be maintained for each C–V characteristic at the corresponding etching depth. The method allows both to obtain general free charge carrier concentration depth distribution over unlimited structure depth and to achieve necessary resolution in key regions, even if they are lying at a high depth. Particularly, in GaAs-based heterostructure concentration profile of deep-lying quantum well and a delta layer (ultra-thin (2 nm) profile of dopant with extremely high concentration) were confidently distinguished with magnitude difference of 20% in the dip region in between. Besides, we have shown how quantum-confined objects of different origins give different concentration responses when electrochemical capacitance–voltage profiling with sequential etching. An explanation for this dissimilarity is given.



中文翻译:

电化学电容-电压特性的集成:一种获得高分辨率的自由载流子深度分布曲线的新程序

提出,开发和证明了一种包含积分电容-电压特性的新型电化学分析技术。目的是要克服在常用的电化学电容-电压分布图过程中电荷载流子浓度测量中不足的分辨率。对于现代低维光电来说,这是非常实际的任务。根据引入的“轮廓窗”,在每个蚀刻步骤之后,选择坐标间隔,其中浓度分布将自身匹配并且由边缘处的一阶导数与相邻间隔中的浓度匹配。为达到这些条件,应在相应的刻蚀深度处为每个C–V特性保持最小耗散因数。该方法既可以在不受限制的结构深度上获得一般的自由电荷载流子浓度深度分布,又可以在关键区域实现必要的分辨率,即使它们位于较高的深度也是如此。尤其是,在基于GaAs的异质结构中,深层量子阱和δ层(极高浓度的掺杂物的超薄(2 nm)轮廓)可以可靠地加以区分,在两者之间的倾角区域幅度差异为20%。 。此外,我们还显示了当通过顺序蚀刻进行电化学电容-电压分布分析时,不同来源的量子限制对象如何产生不同的浓度响应。给出了这种差异的解释。即使他们躺在很深的地方。尤其是,在基于GaAs的异质结构中,深层量子阱和δ层(极高浓度的掺杂物的超薄(2 nm)轮廓)可以可靠地加以区分,在两者之间的倾角区域幅度差异为20%。 。此外,我们还显示了当通过顺序蚀刻进行电化学电容-电压分布分析时,不同来源的量子限制对象如何产生不同的浓度响应。给出了这种差异的解释。即使他们躺在很深的地方。尤其是,在基于GaAs的异质结构中,深层量子阱和δ层(极高浓度的掺杂物的超薄(2 nm)轮廓)可以可靠地加以区分,在两者之间的倾角区域幅度差异为20%。 。此外,我们还显示了当通过顺序蚀刻进行电化学电容-电压分布分析时,不同来源的量子限制对象如何产生不同的浓度响应。给出了这种差异的解释。我们已经展示了当使用顺序蚀刻进行电化学电容-电压分布分析时,不同来源的量子限制对象如何产生不同的浓度响应。给出了这种差异的解释。我们已经展示了当使用顺序蚀刻进行电化学电容-电压分布分析时,不同来源的量子限制对象如何产生不同的浓度响应。给出了这种差异的解释。

更新日期:2020-11-06
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