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    • 5. 发明授权
    • Design of microelectronic process flows for manufacturability and performance
    • 微电子工艺流程的可制造性和性能设计
    • US06311096B1
    • 2001-10-30
    • US09283479
    • 1999-04-01
    • Sharad SaxenaKarthik VasanthRichard G. BurchPurnendu K. MozumderSuraj RaoJoseph C. Davis
    • Sharad SaxenaKarthik VasanthRichard G. BurchPurnendu K. MozumderSuraj RaoJoseph C. Davis
    • G06F1900
    • G06Q10/06
    • A statistical design method is provided for minimizing the impact of manufacturing variations on semiconductor manufacturing by statistical design which seeks to reduce the impact of variability on device behavior. The method is based upon a Markov representation of a process flow which captures the sequential and stochastic nature of semiconductor manufacturing and enables the separation of device and process models, statistical modeling of process modules from observable wafer states and approximations for statistical optimization over large design spaces. The statistical estimation component of this method results in extremely accurate predictions of the variability of transistor performance for all of the fabricated flows. Statistical optimization results in devices that achieve all transistor performance and reliability goals and reduces the variability of key transistor performances.
    • 提供统计设计方法,用于通过统计设计来最小化制造差异对半导体制造的影响,其旨在减少变异性对器件行为的影响。 该方法基于处理流程的马尔科夫表示,其捕获半导体制造的顺序和随机性质,并且能够分离设备和过程模型,将过程模块从可观察的晶片状态进行统计建模以及用于大型设计空间的统计优化的近似 。 该方法的统计估计分量导致了对所有制造流量的晶体管性能的可变性的非常准确的预测。 统计优化可以实现所有晶体管性能和可靠性目标的器件,并减少关键晶体管性能的变化。
    • 9. 发明授权
    • Method and system for using response-surface methodologies to determine optimal tuning parameters for complex simulators
    • 使用响应面方法确定复杂模拟器的最佳调谐参数的方法和系统
    • US06381564B1
    • 2002-04-30
    • US09304189
    • 1999-05-03
    • Joseph C. DavisKarthik VasanthSharad SaxenaPurnendu K. MozumderSuraj RaoChenjing L. FernandoRichard G. Burch
    • Joseph C. DavisKarthik VasanthSharad SaxenaPurnendu K. MozumderSuraj RaoChenjing L. FernandoRichard G. Burch
    • G06F945
    • G06F17/5036
    • A method and system for providing optimal tuning for complex simulators. The method and system include initially building at least one RSM model having input and output terminals. Then there is provided a simulation-free optimization function by constructing an objective function from the outputs at the output terminals of the at least one RSM model and experimental data. The objective function is optimized in an optimizer and the optimized objective function is fed to the input terminal of the RSM. Building of at least one RSM model includes establishing a range for the simulation, running a simulation experiment for the designed experiment, extracting relevant data from said experiment and building the RSM model from the extracted relevant data. The step of running a simulation experiment comprises the step of running a DOE/Opt operation. The objective function is, for example, the square root of the sum of the squares at all of the differences between the target values and the observed values at all points being investigated. The optimizing in the optimizer is preferably a gain function which adjusts the parameters to the input based upon the error signal to minimize the error signal.
    • 一种用于为复杂模拟器提供最佳调谐的方法和系统。 该方法和系统包括最初构建至少一个具有输入和输出端的RSM模型。 然后,通过从至少一个RSM模型的输出端子和实验数据的输出构造目标函数来提供无模拟优化功能。 目标函数在优化器中进行优化,优化的目标函数馈送到RSM的输入端。 建立至少一个RSM模型,包括建立模拟范围,运行设计实验的仿真实验,从所提取的实验中提取相关数据,并从提取的相关数据构建RSM模型。 运行模拟实验的步骤包括运行DOE / Opt操作的步骤。 目标函数例如是目标值与被调查的所有点的观测值之间的所有差异的平方和的平方根。 优化器中的优化优选地是基于误差信号调整输入参数的增益函数,以最小化误差信号。