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    • 1. 发明授权
    • De novo processing of electronic materials
    • 从头处理电子材料
    • US06685772B2
    • 2004-02-03
    • US10113919
    • 2002-03-28
    • William A. Goddard, IIIGyeong S. Hwang
    • William A. Goddard, IIIGyeong S. Hwang
    • C30B110
    • G06F17/5018
    • Computer programs and computer-implemented methods for predicting from first principles the behavior of dopants and defects in the processing of electronic materials. The distribution of dopant and defect components in a substrate lattice is predicted based on external conditions and fundamental data for a set of microscopic processes that can occur during material processing operations. The concentration behavior of one or more fast components is calculated in two stages, by solving a first relationship for a time period before the fast component reaches a pseudo steady state at which the concentration of the fast component is determined by concentrations of one or more second components, and by solving a second relationship for a time period after the first component reaches the pseudo steady state. Application of these methods to modeling ultrashallow junction processing is also described.
    • 计算机程序和计算机实现的方法,用于从第一原理预测掺杂剂的行为和电子材料加工中的缺陷。 掺杂剂和缺陷组分在衬底晶格中的分布是基于在材料加工操作期间可能发生的一组微观工艺的外部条件和基本数据来预测的。 一个或多个快速组分的浓度行为通过在快速组分达到假稳定状态之前的时间段内求解第一关系来计算两个阶段,其中快速组分的浓度由一个或多个第二个浓度确定 并且通过在第一分量达到伪稳定状态之后的时间段内求解第二关系。 还描述了这些方法对超浅接合处理建模的应用。
    • 4. 发明授权
    • Method for predicting the behavior of dopant and defect components
    • 用于预测掺杂剂和缺陷组分的行为的方法
    • US07074270B2
    • 2006-07-11
    • US10406033
    • 2003-04-02
    • Yuzuru SatoMasamitsu UeharaGyeong S. HwangWilliam A. Goddard, III
    • Yuzuru SatoMasamitsu UeharaGyeong S. HwangWilliam A. Goddard, III
    • C30B1/00
    • H01L21/26513G06F17/5018G06F2217/10H01L21/26566H01L21/2658
    • Techniques for predicting the behavior of dopant and defect components in a substrate lattice formed from a substrate material can be implemented in hardware or software. Fundamental data for a set of microscopic processes that can occur during one or more material processing operations is obtained. Such data can include data representing the kinetics of processes in the set of microscopic processes and the energetics and structure of possible states in the material processing operations. From the fundamental data and a set of external conditions, distributions of dopant and defect components in the substrate lattice are predicted. The distributions of one or more fast components are each predicted by calculating the concentration of the particular fast component for a time period before that fast component reaches its pseudo steady state by solving a first relationship and calculating the concentration of that fast component after the time period by solving a second relationship based on other components, the pseudo steady state of a fast component being a state in which the concentration of that fast component is determined by concentrations of other components. The distribution of Bs3Bi, in addition to the distributions of Bs, BsI, BsI2, BsI3, BsBi, BsBi2, BsBi3, BsBiI, BsBiI2, Bs2Bi, Bs2Bi2, I and In, are calculated by solving the first relationship to predict the distribution of boron after annealing, where Bs and Bi represent substitutional boron and interstitial boron, respectively, and I and In represent interstitial silicon and a cluster of n I's, respectively.
    • 用于预测由衬底材料形成的衬底晶格中的掺杂剂和缺陷组分的行为的技术可以在硬件或软件中实现。 获得可在一个或多个材料加工操作期间发生的一组微观工艺的基本数据。 这样的数据可以包括表示微观过程集合中的过程的动力学的数据,以及材料处理操作中可能状态的能量学和结构。 从基础数据和一组外部条件,预测衬底晶格中的掺杂剂和缺陷组分的分布。 一个或多个快速分量的分布各自通过在该快速分量达到其伪稳态之前的时间段内计算特定快速分量的浓度来求解第一关系并计算该时间段之后该快分量的浓度 通过解决基于其他成分的第二关系,快速成分的伪稳定状态是通过其他成分的浓度来确定该快速成分的浓度的状态。 除了Bs,BsI,BsI2,BsI3,BsBi,BsBi,BsBi,BsBi, 2,BsBi 3,BsBiI,BsBi1 2,Bs2 Bi,Bs2 Bi, 通过求解第一关系来计算退火后硼的分布,其中Bs和Bi分别代表取代硼和间隙硼,并且 I和I分别表示间隙硅和n I的簇。