阅读说明：本技术 用于优化光刻工艺窗口的方法及装置、计算机存储介质 (Method and device for optimizing photoetching process window and computer storage medium ) 是由 张双 韦亚一 张利斌 盖天洋 何建芳 于 2021-01-11 设计创作，主要内容包括：本发明能够提供用于优化光刻工艺窗口的方法及装置、计算机存储介质。用于优化光刻工艺窗口的方法包括：获取理想光刻条件下的理想光源信息和理想掩模信息,依据理想光源信息和理想掩模信息生成理想光刻条件下的工艺窗口信息；基于工艺窗口信息得到用于优化当前光刻工艺窗口的像差系数组合；其中,在当前光刻过程中使用与理想光源信息对应的光源以及与理想掩模信息对应的掩模。本发明能够快速地确定像差系数组合,以利用该像差系数组合提高光刻成像质量,进而提高半导体器件制造的良率。本发明能够有效解决非理想光刻机系统或非理想工艺条件下的光刻工艺匹配问题,可达到与理想光刻机、理想光刻工艺相当的工艺窗口。(The invention can provide a method and a device for optimizing a photoetching process window and a computer storage medium. The method for optimizing the photolithography process window comprises: acquiring ideal light source information and ideal mask information under ideal photoetching conditions, and generating process window information under the ideal photoetching conditions according to the ideal light source information and the ideal mask information; obtaining an aberration coefficient combination for optimizing a current photoetching process window based on the process window information; wherein a light source corresponding to ideal light source information and a mask corresponding to ideal mask information are used in the current photolithography process. The invention can quickly determine the aberration coefficient combination so as to improve the photoetching imaging quality by utilizing the aberration coefficient combination and further improve the yield of semiconductor device manufacturing. The invention can effectively solve the matching problem of the photoetching process under the non-ideal photoetching machine system or the non-ideal process condition, and can achieve the process window equivalent to that of an ideal photoetching machine and an ideal photoetching process.)

1. A method for optimizing a photolithography process window, comprising:

acquiring ideal light source information and ideal mask information under ideal photoetching conditions;

generating process window information under the ideal photoetching condition according to the ideal light source information and the ideal mask information;

obtaining an aberration coefficient combination for optimizing a current photoetching process window based on the process window information; wherein a light source corresponding to the ideal light source information and a mask corresponding to the ideal mask information are used in a current photolithography process.

2. The method of claim 1, wherein obtaining ideal light source information and ideal mask information under ideal lithography conditions comprises:

reading initial light source information and initial mask information;

and performing light source mask collaborative optimization under the ideal photoetching condition so as to optimize the initial light source information into ideal light source information and optimize the initial mask information into ideal mask information.

3. The method of claim 1 or 2, wherein deriving the aberration coefficient combination for optimizing the current lithography process window based on the process window information comprises:

receiving the process window information by utilizing a machine learning model which is trained previously, wherein the machine learning model inputs the process window information and outputs the process window information as an aberration coefficient combination;

and processing the process window information through the machine learning model to obtain the aberration coefficient combination.

4. The method for optimizing a lithography process window of claim 3, wherein the machine learning model is a neural network model.

5. The method of claim 4, wherein the obtaining a combination of aberration coefficients after processing the process window information through the machine learning model comprises:

the process window information is sent to an input layer of a neural network model, and the neural network model comprises an input layer, a hidden layer and an output layer which are sequentially connected;

processing the process window information by utilizing a hidden layer of a neural network model;

and outputting the aberration coefficient combination obtained by processing the process window information through an output layer of the neural network model.

6. The method for optimizing a lithographic process window according to claim 1 or 2, wherein said aberration coefficient combination comprises a plurality of Zernike polynomial coefficients.

7. The method for optimizing a photolithography process window of claim 6, wherein the plurality of Zernike polynomial coefficients are Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, Z10, Z11, Z12, Z13, Z14, Z15, Z16, respectively.

8. The method of claim 1 or 2, wherein the process window information comprises depth of focus, mask error enhancement factor, and light intensity log slope.

9. An apparatus for optimizing a photolithography process window, comprising:

the ideal light source mask acquisition module is used for acquiring ideal light source information and ideal mask information under ideal photoetching conditions;

the process window information generating module is used for generating process window information under the ideal photoetching condition according to the ideal light source information and the ideal mask information;

the aberration coefficient combination determining module is used for obtaining an aberration coefficient combination for optimizing the current photoetching process window based on the process window information; wherein a light source corresponding to the ideal light source information and a mask corresponding to the ideal mask information are used in a current photolithography process.

10. A computer storage medium, characterized in that the computer storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for optimizing a lithographic process window according to any one of claims 1 to 8.

Technical Field

The present invention relates to the field of lithography process window optimization technology, and more particularly, to a method and apparatus for optimizing a lithography process window, and a computer storage medium.

Background

The photoetching process is a key process and a core process in the integrated circuit processing, and the quality of the photoetching level directly influences the performance of devices and chips. In the photolithography Process, a photolithography Process Window (Process Window) is an extremely important standard for measuring the photolithography level, and relevant developers are always dedicated to optimizing the photolithography Process Window to obtain a larger photolithography Process Window. The performance of the lithography machine during operation has a direct influence on the lithography process window, especially the working state of the lithography machine during exposure.

Under non-ideal lithography conditions, the lens of the projection objective heats up when the lithography machine is in operation. The lens heating of the projection objective of the photoetching machine can cause the dynamic refractive index change, further cause the photoetching process parameters such as the focusing depth and the like to be poor, and finally cause the exposure quality to be reduced.

Disclosure of Invention

In order to solve the problem of poor photoetching process parameters caused by heating of a lens of a projection objective of a photoetching machine, the invention provides a method and a device for optimizing a photoetching process window and a computer storage medium, and the aim of optimizing the photoetching process window and the like can be fulfilled.

To achieve the above technical objects, one or more embodiments of the present invention disclose a method for optimizing a photolithography process window; the method may include, but is not limited to, at least one of the following steps.

And acquiring ideal light source information and ideal mask information under ideal photoetching conditions.

And generating the process window information under the ideal photoetching condition according to the ideal light source information and the ideal mask information.

Obtaining an aberration coefficient combination for optimizing a current photoetching process window based on the process window information; wherein a light source corresponding to the ideal light source information and a mask corresponding to the ideal mask information are used in a current photolithography process.

Further, the acquiring ideal light source information and ideal mask information under ideal lithography conditions includes:

initial light source information and initial mask information are read.

And performing light source mask collaborative optimization under the ideal photoetching condition so as to optimize the initial light source information into ideal light source information and optimize the initial mask information into ideal mask information.

Further, the obtaining an aberration coefficient combination for optimizing a current photolithography process window based on the process window information includes:

and receiving the process window information by utilizing a machine learning model which is trained previously, wherein the machine learning model inputs the process window information and outputs the aberration coefficient combination.

And processing the process window information through the machine learning model to obtain the aberration coefficient combination.

Further, the machine learning model is a neural network model.

Further, the obtaining of the aberration coefficient combination after the processing of the process window information by the machine learning model includes:

and sending the process window information to an input layer of a neural network model, wherein the neural network model comprises an input layer, a hidden layer and an output layer which are sequentially connected.

And processing the process window information by utilizing a hidden layer of the neural network model.

And outputting the aberration coefficient combination obtained by processing the process window information through an output layer of the neural network model.

Further, the aberration coefficient combination includes a plurality of zernike polynomial coefficients.

Further, the plurality of zernike polynomial coefficients are Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, Z10, Z11, Z12, Z13, Z14, Z15, Z16, respectively.

Further, the process window information includes depth of focus, mask error enhancement factor, and light intensity log slope.

To achieve the above technical objects, the present invention can also provide an apparatus for optimizing a photolithography process window, which includes, but is not limited to, an ideal light source mask acquisition module, a process window information generation module, and an aberration coefficient combination determination module.

And the ideal light source mask acquisition module is used for acquiring ideal light source information and ideal mask information under ideal photoetching conditions.

And the process window information generating module is used for generating the process window information under the ideal photoetching condition according to the ideal light source information and the ideal mask information.

The aberration coefficient combination determining module is used for obtaining an aberration coefficient combination for optimizing the current photoetching process window based on the process window information; wherein a light source corresponding to the ideal light source information and a mask corresponding to the ideal mask information are used in a current photolithography process.

To achieve the above technical object, the present invention can also provide a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for optimizing a photolithography process window in any of the embodiments of the present invention.

The invention has the beneficial effects that: the method can quickly determine the aberration coefficient combination so as to improve the photoetching imaging quality by utilizing the aberration coefficient combination, further improve the yield of semiconductor device manufacturing, is favorable for reducing the cost of the semiconductor device, and has higher application value and market value.

The invention can effectively solve the matching problem of the photoetching process under the non-ideal photoetching machine system or the non-ideal process condition, and the relevant parameters of the photoetching equipment lens are adjusted through the obtained aberration coefficient combination so as to achieve the process window equivalent to the ideal photoetching machine and the ideal photoetching process. On the premise of not changing the existing light source and mask, the invention can greatly improve the performance of the photoetching machine and avoid the influence of environmental change factors on the performance of the photoetching machine as much as possible.

The invention innovatively adopts a machine learning method to predict the combination of Zernike polynomial coefficients, and can greatly reduce the dependence on hardware resources. The invention can quickly predict and screen out the combination of Zernike polynomial coefficients meeting the photoetching imaging requirements, thereby reducing the requirements on the lens of the projection objective of the photoetching machine, for example, allowing partial aberration and the like to exist in the lens, being more beneficial to the design of the projection objective of the photoetching machine, and further obviously reducing the photoetching process cost.

Drawings

FIG. 1 shows a schematic diagram of a method for optimizing a lithographic process window in one or more embodiments of the invention.

FIG. 2 shows a schematic diagram of a one-dimensional line structure included in a mask in one or more embodiments of the invention.

FIG. 3 shows a schematic diagram of a two-dimensional hole pattern contained by a mask in one or more embodiments of the invention.

FIG. 4 shows a schematic view of a light source obtained under ideal lithographic conditions in one or more embodiments of the invention.

FIG. 5 illustrates a schematic view of a mask formed under ideal lithographic conditions in one or more embodiments of the invention.

FIG. 6 illustrates a schematic overlay of a process window obtained under ideal lithographic conditions in one or more embodiments of the invention.

FIG. 7 is a schematic diagram illustrating energy margin and depth of focus under ideal lithographic conditions in one or more embodiments of the invention.

FIG. 8 shows a schematic view of an exposure pattern under ideal lithographic conditions in one or more embodiments of the invention.

FIG. 9 is a schematic diagram illustrating an exposure pattern under non-ideal lithographic conditions in one or more embodiments of the invention.

FIG. 10 is a graphical illustration of the effect of Zernike polynomial coefficients on depth of focus under non-ideal lithographic conditions in one or more embodiments of the invention.

FIG. 11 is a graph illustrating the effect of Zernike polynomial coefficients on mask error enhancement factors and intensity log slopes under non-ideal lithography conditions (where mask error enhancement factors are on the left and intensity log slopes are on the right) in one or more embodiments of the invention

FIG. 12 illustrates a schematic structural diagram of a trained neural network model in one or more embodiments of the invention.

FIG. 13 is a diagram illustrating the gradual convergence of the loss function with an increasing number of iterations in one or more embodiments of the invention.

FIG. 14 depicts a distribution diagram of a set of Zernike polynomial coefficient combinations obtained under non-ideal lithographic conditions in one or more embodiments of the invention.

FIG. 15 illustrates a schematic overlay of a process window obtained using the present invention under non-ideal lithographic conditions in one or more embodiments of the present invention.

Detailed Description

The method, apparatus, and computer storage medium for optimizing a photolithography process window according to the present invention are explained and illustrated in detail below with reference to the accompanying drawings.

As shown in FIG. 1, one or more embodiments of the invention provide a method for optimizing a photolithography process window, which may include, but is not limited to, at least one of the following steps.

An initial light source and an initial mask are provided and optimal lithographic conditions can be selected. Wherein the initial light source may include, but is not limited to, a ring light source, a FreeForm light source, and a DOE (Diffractive Optical Elements) light source; the initial mask may include a test mask on which a core size structure is drawn and a chip manufacturing mask for processing a corresponding device and chip, wherein the test mask is mainly used for performing light source mask co-optimization on the core size of the chip.

As shown in fig. 2, the test mask may include, but is not limited to, a one-dimensional periodic structure pattern, a one-dimensional variable dimension pattern, and the like. Such as dense line structures in fig. 2(a), end-to-end structures in fig. 2(b), end-to-line structures in fig. 2(c), and individual line structures in fig. 2(d), etc., the test mask may of course contain one-dimensional line structures, etc., of varying feature sizes and periods.

As shown in FIG. 3, the test mask may also include, but is not limited to, a two-dimensional periodic structure pattern, a two-dimensional variable periodic structure pattern, and a two-dimensional variable size pattern. Such as the dense hole pattern of fig. 3(a), the variable period hole pattern of fig. 3(b), the isolated hole pattern of fig. 3(c), and so on, the test mask may of course contain two-dimensional hole pattern structures in which the feature size and period vary; the invention can be applied to test masks with various structural forms and has wider application range.

Reading the initial light source information and the initial mask information, one or more embodiments of the present invention can use a ring light source as the initial light source and a mask having a one-dimensional structure pattern as the initial mask. Light Source Mask Optimization (SMO) may be performed under ideal lithography conditions to optimize initial light Source information to ideal light Source information and to be able to optimize the initial Mask information to the ideal Mask information. The specific process of the light source mask collaborative optimization can be reasonably selected and adjusted according to actual conditions, and the detailed description is omitted. Therefore, the invention can obtain ideal light source information and ideal mask information under ideal photoetching conditions, namely can obtain the light source and the mask under the ideal photoetching conditions. The light source mask collaborative optimization under the ideal photoetching condition in the invention refers to: the joint optimization of the light source and the mask without taking into account the thermal aberrations caused by heating of the projection objective. In some embodiments of the invention, the sub-resolution auxiliary pattern is added to the independent line during the light source mask collaborative optimization, so that a larger process window can be obtained.

As shown in FIG. 4, one or more embodiments of the present invention provide ideal illuminant information after an initial ring illuminant has been cooperatively optimized with a illuminant mask. The diagram visually illustrates that the initial annulus illuminant information is changed along with the mask information after the illuminant mask co-optimization.

As shown in fig. 5, one or more embodiments of the present invention provide ideal mask information obtained by performing a light source mask co-optimization, the mask information is changed together with the initial light source information, and the mask may include, but is not limited to, a dense line mask, an independent line mask, an end-to-line mask, and an end-to-end mask.

And generating process window information under ideal photoetching conditions according to the ideal light source information and the ideal mask information. When the process is realized through computer simulation, the ideal light source information and the ideal mask information can be determined, and meanwhile, the process window information under the ideal photoetching condition can be determined. The process window information may include, but is not limited to, Depth of Focus (DOF), Mask Error Enhancement Factor (MEEF), and light Intensity Log Slope (ILS).

As shown in FIG. 6, one or more embodiments of the invention provide a process window schematic under ideal lithographic conditions: the exposure dose is less than 1 and the exposure focus is at-50 nm. As shown in FIG. 7, in one or more embodiments of the invention, after performing light source mask co-optimization under ideal lithography conditions, the relevant process parameters, such as 100.32 depth of focus, 5.000 exposure margin, 4.78 mask error enhancement factor, and 17.81 log slope of light intensity, can be obtained.

As shown in fig. 8, the exposure simulation diagram under the ideal lithography condition shows that the exposure pattern coincides with the set position. As shown in fig. 9, when the conventional embodiment is implemented, the exposure simulation result under the non-ideal lithography condition is shown in the figure, and it can be seen that the conventional exposure manner may shift the exposure pattern, for example, to the left as shown (the shift distance exceeds 10% of the critical image dimension value). Therefore, the process window is often sharply reduced after exposure under the existing non-ideal photoetching condition, so that the imaging quality of photoetching is seriously influenced; the present invention can solve this problem.

And obtaining an aberration coefficient combination for optimizing the current photoetching process window based on the process window information. Specifically, the invention can combine the machine learning technology, and utilize the machine learning model which is trained to receive the process window information, the machine learning model inputs the process window information and outputs the aberration coefficient combination, and after the process window information is processed by the machine learning model, the corresponding aberration coefficient combination can be obtained. The aberration coefficient combination in one or more embodiments of the invention may comprise a plurality of Zernike (Zernike) polynomial coefficients, wherein the plurality of Zernike polynomial coefficients may for example be Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, Z10, Z11, Z12, Z13, Z14, Z15, Z16, respectively.

More specifically, the machine learning model in the embodiment of the present invention is a neural network model, which may specifically include, but is not limited to, a fully-connected neural network model. The step of obtaining the aberration coefficient combination after processing the process window information through the machine learning model comprises the following steps: the process window information can be sent to an input layer of a neural network model, and the neural network model comprises an input layer, a hidden layer and an output layer which are sequentially connected; and the hidden layer of the neural network model is used for processing the process window information, and the aberration coefficients obtained by processing the process window information are combined and output through the output layer of the neural network model. Wherein a light source corresponding to ideal light source information and a mask corresponding to ideal mask information are used in the current photolithography process. It can be understood that there are numerous aberration coefficient combinations, and the result achieved by the present invention can be equivalent to quickly screening one aberration coefficient combination from the aberration coefficient combinations for the current photolithography process.

As shown in fig. 12, the present invention may be implemented by training a neural network model. The training process may include: the method comprises the steps of establishing a neural network model, randomly generating 500 sets of Zernike polynomial coefficient combinations from the range of-0.025 lambda to 0.025 lambda, and calculating the DOF, MEEF and ILS of each combination under the light source mask collaborative optimization condition to obtain a training set and a verification set of the model, wherein for example, 100 sets of Zernike polynomial coefficient combinations and corresponding DOF, MEEF and ILS contained in the training set and the verification set can be used as the verification set. The method can control the numerical value of partial Zernike polynomial coefficient to be larger than 0.001 lambda so as to improve the robustness of the neural network model.

As shown in fig. 13, in the process of training the neural network model, the loss function is converged in an iterative manner to complete the training of the neural network model. In specific application, the invention can reversely utilize the trained neural network model, namely, the DOF, MEEF and ILS are used as input to obtain the corresponding Zernike polynomial coefficient combination (when reversely utilizing, the output layer during training is used as the input layer and the input layer during training is used as the output layer); or after the loss function is converged, the DOF, the MEEF, the ILS and the corresponding combination of the Zernike polynomial coefficients are used for training another newly-built neural network model, and then the combination of the Zernike polynomial coefficients (namely the combination of the aberration coefficients required by the invention) can be determined by using the newly-built neural network model trained at this time.

As shown in the table below, the present invention is able to verify the results to improve the reliability of the invention. When the loss function is minimal, 10 sets of randomly generated zernike polynomial coefficient combinations and validation results are collected.

Z2	-0.02413	-0.00712	-0.00689	-0.03286	-0.00742	0.012746	-0.01613	-0.01861	-0.01827	-0.02905
											Z3	0.012978	-0.00274	0.01274	0.010757	0.012671	0.021095	-0.01014	0.012471	0.000045	0.020074
Z4	0.038137	-0.01024	0.004628	-0.03939	-0.00322	-0.0094	-0.01612	0.00545	-0.00368	0.004797
											Z5	-0.03419	-0.04006	0.006979	-0.03822	0.019443	0.03168	-0.00685	-0.01912	-0.01234	-0.01651
Z6	0.016106	-0.03152	0.006917	0.033521	0.002113	-0.02176	0.026556	0.034729	-0.01129	0.020794
											Z7	0.001026	0.000932	-0.0014	0.030987	-0.01978	0.00447	0.028941	3.42E-05	-0.03659	0.028534
Z8	-0.01763	0.042915	0.0037	0.021068	0.016598	0.014569	-0.01801	0.015124	0.002022	0.004355
											Z9	0.000289	0.040556	0.021695	0.047841	-0.00409	-0.0057	0.000164	0.007923	-0.01024	0.013363
Z10	0.013542	0.006846	0.003166	-0.00417	0.012545	-0.02404	0.016393	0.011184	0.012247	0.009362
											Z11	-0.03362	-0.01244	-0.00533	-0.06043	0.002211	-0.00681	-0.0201	-0.02925	-0.03649	-0.02752
Z12	0.009905	0.008828	0.003733	-0.00487	-0.02086	-0.00824	-0.04837	0.003762	-0.00057	-0.00149
											Z13	-0.03145	0.006697	0.016161	-0.04979	-0.09373	-0.01507	-0.00651	0.002699	-0.00752	-0.00465
Z14	0.005645	-0.02529	0.003308	0.006126	-0.0123	0.01692	-0.02277	0.012252	0.008422	-0.01217
											Z15	-0.01737	-0.04073	0.004204	0.040211	0.003159	-0.01423	-0.02464	-0.02269	-0.02744	-0.03054
Z16	-0.02648	-0.0371	-0.00054	0.005567	-0.00113	0.000859	0.002656	-0.0026	0.015579	0.020194
											DOF	92.26	58	103.06	0	91.58	95.5	94.62	79.98	91.36	96.7
MEEF	5.18	5.55	5.09	5.65	5.38	5.18	5.39	5.05	5.17	5.04
											ILS	17.36	17.41	16.68	17.41	16.21	15.87	15.07	18.03	16.72	18.77

As shown in fig. 10 and 11, the present invention takes fifteen zernike polynomial coefficients as an example to illustrate that the lithographic process parameters can be influenced by the variation of the zernike polynomial coefficients. In this example of the invention, Z5 and Z12 have the greatest effect on depth of focus, decreasing depth of focus, similarly Z7 and Z15 increase the mask error enhancement factor and Z10 and Z4 decrease the intensity log slope, thus demonstrating: the invention can improve the parameter of the photoetching process by adjusting the coefficient of the Zernike polynomial.

As shown in fig. 14, the embodiment of the present invention shows the aberration coefficient combination that can make the actual lithography process window and the ideal lithography process window the closest, and the combination of the zernike polynomial coefficients can make the lithography process window under the current non-ideal lithography condition equal to the lithography process window under the ideal condition.

As shown in fig. 15, under non-ideal lithography conditions, the figure shows the process window result of lithography using the optimal combination of zernike polynomial coefficients obtained by the present invention, from which it can be seen that the exposure dose is significantly increased and the lithography process parameters are better.

Based on the obtained aberration coefficient combination, the method can reasonably control relevant variable parameters such as lens aberration, light intensity, focus value and the like of the lens of the photoetching equipment so as to achieve a process window equivalent to an ideal photoetching machine and an ideal photoetching process.

The present invention also enables an apparatus for optimizing a lithographic process window that may include, but is not limited to, an ideal light source mask acquisition module, a process window information generation module, and an aberration coefficient combination determination module.

And the ideal light source mask acquisition module is used for acquiring ideal light source information and ideal mask information under ideal photoetching conditions.

Wherein, the invention reads the initial light source information and the initial mask information. The light source mask co-optimization is performed under ideal lithography conditions to optimize initial light source information to ideal light source information and to optimize initial mask information to ideal mask information.

And the process window information generating module is used for generating the process window information under the ideal photoetching condition according to the ideal light source information and the ideal mask information. The process window information in the present invention may include, but is not limited to, depth of focus, mask error enhancement factor, and light intensity log slope.

The aberration coefficient combination determining module is used for obtaining an aberration coefficient combination used for optimizing the current photoetching process window based on the process window information; wherein a light source corresponding to ideal light source information and a mask corresponding to ideal mask information are used in the current photolithography process. Specifically, the aberration coefficient combination determining module receives process window information by using a machine learning model trained in advance, the machine learning model inputs the process window information and outputs the process window information as an aberration coefficient combination, and the machine learning model processes the process window information to obtain the aberration coefficient combination. The aberration coefficient combination in the embodiment of the invention comprises a plurality of Zernike polynomial coefficients which are respectively Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, Z10, Z11, Z12, Z13, Z14, Z15 and Z16.

One or more of the present invention can also provide a computer storage medium, where a computer program is stored on the computer storage medium, and when the computer program is executed by a processor, the steps of the method for optimizing a photolithography process window in any embodiment of the present invention are implemented, where the steps of the method for specifically optimizing a photolithography process window are described in other embodiments of the present invention, and are not described in detail in this embodiment.

The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM-Only Memory, or flash Memory), an optical fiber device, and a portable Compact Disc Read-Only Memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic Gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic Gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

In the description herein, references to the description of the term "the present embodiment," "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and simplifications made in the spirit of the present invention are intended to be included in the scope of the present invention.