Data processing: generic control systems or specific application – Specific application – apparatus or process – Product assembly or manufacturing
Reexamination Certificate
1999-01-05
2001-11-13
Grant, William (Department: 2121)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Product assembly or manufacturing
C700S044000, C700S121000
Reexamination Certificate
active
06317640
ABSTRACT:
TECHNICAL FIELD OF THE INVENTION
This invention relates in general to the field of electronic devices, and more particularly, to an improved system and method for non-parametric modeling of processed-induced variability.
BACKGROUND OF THE INVENTION
The trend towards small feature sizes in solid state integrated circuits has increased the need to accurately characterize the distribution of process and device responses to predict and improve design for manufacturability. The usual approach to characterization assumes that the response distributions have a parametric form or normal distribution. For example, in the designed experiment with a limited number of design points, it is possible to build response surface models (RSM) for the mean and variance as a function of process settings. This model is then used to predict the mean and variance at an arbitrary point in the design space. This information, and the assumption of normality, compactly characterizes the predicted response distribution at the interpolated point in the design space.
One problem with the traditional approach is that the response data may not have a normal or a closed form distribution. This may be due to the inherent features of the measured data or due to spatial dependencies of measurements with a particular experimental structure such as a semiconductor wafer. For example, in experiments measuring the salicidation process which involves the formation of silicide region using a self-aligned process, the design characteristics of contact resistance and sheet resistance were found to be functions of specific process settings such as implant dose, titanium thickness, anneal temperature, and anneal time. However, the experimental data measuring the contact resistance and sheet resistance did not follow any standard parametric distribution. As a result, estimation of process variability at an interpolated point within the design space is extremely difficult.
SUMMARY OF THE INVENTION
Accordingly, a need has arisen for a process modeling system and method that addresses the non-parametric data distributions associated with current process technologies.
In accordance with the teachings of the present invention, a modeling system and method are provided that substantially eliminate and reduce problems associated with prior systems and techniques. According to one embodiment of the present invention, a modeling method is disclosed that provides a non-parametric approach to address the problems with conventional systems. According to the teachings of the present invention, the experimental data is first used to build response surface models for the mean and variance of the experimental data at various points within the design space. Next, an empirical cumulative distribution function (CDF) is generated that represents the overall shape of the data by accumulating the shape evidence from each of the available design points. The umulative distribution function and the response surface models for the mean and variance allows the experimental data to be interpolated over the entire design space and allows the process induced variability to be compactly represented using the cumulative distribution function.
REFERENCES:
patent: 5301118 (1994-04-01), Heck et al.
patent: 5646870 (1997-07-01), Krivokapic et al.
patent: 6088658 (2000-07-01), Yazici et al.
Apte Pushkar P.
Burch Richard Gene
Davis Joseph Carl
Fernando Chenjing L.
Mozumder Purnendu K.
Bahta Kidest
Grant William
Texas Instruments Incorporated
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