Interface and gate line edge roughness effects on intra die variance in mos device characteristics

N. Gunther, E. Hamadeh, D. Niemann, M. Rahman
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引用次数: 5

Abstract

Random fluctuations in fabrication process outcomes such as Si-SiO2 interface surface roughness (SR) and gate line edge roughness (LER) give rise to corresponding fluctuations in scaled down MOS device characteristics. These fluctuations are intra-die and inherent even to ideal processes. As such, they represent fiudamental limitations to the die-level uniformity of the properties of otherwise identical devices. Modeling based on statistical characterization of fluctuations in the characteristics of small 3D devices is becoming increasingly important to understand the implications for product designers and process engineers. [1-5] Presently, TCAD numerical simulation is the only tool available for investigating the complex interaction of these issues. In this work, we employ a novel device modeling approach based on thennodynamics and on variational mathematical methods. [6] We obtain closed-form expressions for threshold voltage (Vth), and device capacitance (C) at Onset of Strong Inversion (OSI) for MOS devices in the deep sub-0.1 micron regime. In our model SR is assumed to affect only the gate area whereas LER affects only the gate perimeter of the device. Figure 1 shows the SEM of the fabricated line used in our analysis of SR and LER. [7-8] We take the roughness of this line to be representative and characterize it by Fourier transforming the digitized data. By choosing a pdf to represent the entire spectrm we can then identify the average frequency and the variance. Figure 2 shows the FFT of the digitized data (circles) from Fig. 1, together with the lognormal pdf (solid line) used to fit the data. We have looked in some detail at three possible candidate pdfs: exponential, gaussian, and lognormal. For a combination of reasons we prefer the lognormal. [9] Our variational model predicts that the random deviation of threshold voltage due to LER should increase as the square of the roughness amplitude. Figure 3 shows this variation for a MOSCAP of gate length 35 nm and a width of 50 nm. Our results are compared here with results from Kim et al. [10] In both cases the variation appears to be quadratic in roughness amplitude. Figure 4 shows our prediction of the random deviation of total capacitance of the device at OSI for both LER and SR against standard deviation of roughness wavenumber. Figure 5 shows the random deviation of Vt for LER and SR against average roughness wavenumber according to our model. These statistical characteristics are extremely difficult to capture using TCAD. In contrast, the novelty and strong advantage of our modeling approach is that it allows us to treat the situation with less difficulty by explicitly incorporating these statistical quantities. Oxide thickness is one of the key parameters that affect the variance in Vh. Figure 6 shows the effect of variation in the oxide thickness on the random deviation of Vh according to our model. The model predicts that the deviation is greater for LER than for SR. Interestingly, the deviation in Vh due to roughness reduces drastically for oxide thicknesses less than 4 nm.
界面和栅极线边缘粗糙度对mos器件特性中模内变化的影响
Si-SiO2界面表面粗糙度(SR)和栅极线边缘粗糙度(LER)等制造工艺结果的随机波动会导致相应的缩小MOS器件特性的波动。这些波动是模内的,甚至是理想工艺所固有的。因此,它们代表了对其他相同器件的性能的模级均匀性的基本限制。基于小型3D设备特性波动的统计特征建模对于了解产品设计师和工艺工程师的影响变得越来越重要。[1-5]目前,TCAD数值模拟是唯一可用于研究这些问题复杂相互作用的工具。在这项工作中,我们采用了一种基于非动力学和变分数学方法的新型器件建模方法。[6]我们得到了深亚0.1微米范围内MOS器件的阈值电压(Vth)和器件电容(C)在强反转(OSI)开始时的封闭表达式。在我们的模型中,假设SR只影响栅极面积,而LER只影响器件的栅极周长。图1显示了我们在SR和LER分析中使用的装配线的SEM。[7-8]我们将这条线的粗糙度作为代表性,并对数字化数据进行傅里叶变换表征。通过选择一个pdf来表示整个频谱,我们可以确定平均频率和方差。图2显示了图1中数字化数据(圆)的FFT,以及用于拟合数据的对数正态pdf(实线)。我们详细研究了三种可能的pdf格式:指数格式、高斯格式和对数正态格式。出于多种原因,我们更喜欢对数正态。[9]我们的变分模型预测,由于LER引起的阈值电压的随机偏差应随着粗糙度幅度的平方而增加。图3显示了栅极长度为35 nm,宽度为50 nm的MOSCAP的变化。我们的结果在这里与Kim等人的结果进行了比较。[10]在这两种情况下,粗糙度幅度的变化似乎是二次的。图4显示了我们对LER和SR在OSI下器件总电容随粗糙度波数标准偏差的随机偏差的预测。图5显示了根据我们的模型,LER和SR的Vt对平均粗糙度波数的随机偏差。使用TCAD很难捕捉到这些统计特征。相比之下,我们的建模方法的新颖性和强大优势在于,它允许我们通过显式地合并这些统计量来更容易地处理情况。氧化层厚度是影响Vh变化的关键参数之一。图6显示了根据我们的模型,氧化物厚度的变化对Vh随机偏差的影响。该模型预测LER的偏差大于sr。有趣的是,当氧化物厚度小于4 nm时,由粗糙度引起的Vh偏差急剧减小。
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