Statistical design attribute identification for FinFET outlier and Silicon-to-SPICE gap

机译：FINFET异常值和硅与香料间隙的统计设计属性识别

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From FinFET test vehicle data, outlier is discriminated based on Mahalanobis distance, large Silicon-to-SPICE gap (S2S gap) device is defined, and significant design attributes are analyzed as the root causes of them. In order to analyze this, attribute commonality is defined and calculated based on Monte-Carlo method to identify specific design attributes which are commonly and specifically observed in outlier and large S2S gap device. By narrowing down the design attributes by decision tree using the attribute commonality as decision criterion, the representative structure is identified. The methodology was applied to 14nm and 10nm FinFET with variety of actual layout structures used in product chip.

机译：从FinFET测试车辆数据，基于Mahalanobis距离对异常值进行区分，定义了大的硅 - Spice间隙（S2S间隙）设备，并且将显着的设计属性作为它们的根本原因分析。为了分析这一点，基于Monte-Carlo方法来定义和计算属性共性，以识别在异常值和大S2S间隙装置中通常和专门地观察到的特定设计属性。通过使用属性共性作为决策标准来缩小设计属性，识别代表性结构。该方法应用于14nm和10nm FinFET，具有产品芯片中使用的各种实际布局结构。

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《IEEE International System-on-Chip Conference》|2016年|1 v.|共6页
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Hyosig Won; Katsuhiro Shimazu;
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中图分类通信;
关键词
Gaussian distribution; SPICE; Layout; FinFETs; Standards; Market research; Monte Carlo methods;

机译：高斯分布;香料;布局;FINFET;标准;市场研究;蒙特卡罗方法;

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1. Statistical Variability and Reliability and the Impact on Corresponding 6T-SRAM Cell Design for a 14-nm Node SOI FinFET Technology [J] . Xingsheng Wang, Binjie Cheng, Kuang J.B., Design & Test,IEEE . 2014,第6期

机译：统计可变性和可靠性以及对14nm节点SOI FinFET技术的相应6T-SRAM单元设计的影响
2. Statistical Design Optimization of FinFET SRAM Using Back-Gate Voltage [J] . Ebrahimi B. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on . 2011,第10期

机译：利用背栅电压优化FinFET SRAM的统计设计优化
3. Outlier Identification of Concentrations of Pollutants in Environmental Data Using Modern Statistical Methods [J] . Veselik Petr, Sejkorova Marie, Nieoczym Aleksander, Polish Journal of Environmental Studies . 2020,第1PTa2期

机译：使用现代统计方法识别环境数据中污染物浓度的差异
4. Statistical design attribute identification for FinFET outlier and Silicon-to-SPICE gap [C] . Hyosig Won, Katsuhiro Shimazu IEEE International System-on-Chip Conference . 2016

机译：FinFET离群值和Silicon-to-SPICE间隙的统计设计属性识别
5. Bandgap Reference Design at the 14-Nanometer FinFET Node. [D] . Prilenski, Lucas J. 2017

机译：14纳米FinFET节点的带隙参考设计。
6. Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic [O] . Jinghua Gu, Jianhua Xuan, Rebecca B. Riggins, -1

机译：使用基于异常值和统计量的Gibbs采样器可靠地识别转录调控网络
7. Figure 10: Outliers plots: (A) boxplot with the results of the best three methods (ENET, FSMKL and SVM-RFE) with and without the outliers. *, Statistically significant difference with a p-value < 2.2 × 10−16 according to a pairwise Wilcoxon test; **, Statistically significant difference according to a non-parametric Friedman test with Iman and Davenport correction with a p-value < 1.92 × 10−41. At the bottom of the panel, a median-based contrast estimation heatmap. In (B) the residues are plotted by coloring each of them according to the category to which the image belongs. [O] . -1

机译：图10：异常值绘图：（a）Boxplot与具有和没有异常值的最佳三种方法（ENET，FSMK1和SVM-RFE）的结果。 *，根据双氟硅酮试验，与p值<2.2×10-16的统计学上有显着差异; **，根据非参数弗里德曼测试的统计学意义差异，具有P值<1.92×10-41的IMAN和DAVENPORT校正。在面板的底部，基于中位的对比度估计热图。在（b）中，通过根据图像所属的类别着色，通过着色它们来绘制残留物。
8. A statistical technique for computer identification of outliers in multivariate data [R] . Swaroop, R., Winter, W. R. 1971

机译：计算机识别多变量数据中异常值的统计技术

Statistical design attribute identification for FinFET outlier and Silicon-to-SPICE gap

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