1. A method for decomposing a target circuit pattern containing features to be printed on a wafer, into multiple patterns, comprising the steps of:
separating said features to be printed into a first pattern and a second pattern;
performing a first optical proximity correction process on said first pattern and said second pattern;
determining an imaging performance of said first pattern and said second pattern;
determining a first error between said first pattern and said imaging performance of said first pattern, and a second error between said second pattern and said imaging performance of said second pattern;
utilizing said first error to adjust stitching areas associated with said first pattern to generate a modified first pattern;
utilizing said second error to adjust stitching areas associated with said second pattern to generate a modified second pattern; and
applying a second optical proximity correction process to said modified first pattern and said modified second pattern.
2. A method for decomposing a target circuit pattern according to claim 1, wherein said features are separated into said first pattern and said second pattern utilizing a rule-based decomposition process.
3. A method for decomposing a target circuit pattern according to claim 1, wherein said features are separated into said first pattern and said second pattern utilizing a model-based decomposition process.
4. A method for decomposing a target circuit pattern according to claim 1, wherein said first optical proximity correction process and said second optical proximity correction process are the same processes.
5. A method for decomposing a target circuit pattern according to claim 4, wherein said first optical proximity correction process and said second optical proximity correction utilizes a rule-based correction process.
6. A method for decomposing a target circuit pattern according to claim 4, wherein said first optical proximity correction process and said second optical proximity correction utilize a model-based correction process.
7. A method for decomposing a target circuit pattern according to claim 1, wherein said first error and said second error are determined in said stitching areas associated with said first pattern and said second pattern.
8. A computer readable storage medium storing a computer program for decomposing a target circuit pattern containing features to be printed on a wafer, into multiple patterns, when executed, causing a computer to perform the steps of:
separating said features to be printed into a first pattern and a second pattern;
performing a first optical proximity correction process on said first pattern and said second pattern;
determining an imaging performance of said first pattern and said second pattern;
determining a first error between said first pattern and said imaging performance of said first pattern, and a second error between said second pattern and said imaging performance of said second pattern;
utilizing said first error to adjust stitching areas associated with said first pattern to generate a modified first pattern;
utilizing said second error to adjust stitching areas associated with said second pattern to generate a modified second pattern; and
applying a second optical proximity correction process to said modified first pattern and said modified second pattern.
9. The computer readable storage medium according to claim 8, wherein said features are separated into said first pattern and said second pattern utilizing a rule-based decomposition process.
10. The computer readable storage medium according to claim 8, wherein said features are separated into said first pattern and said second pattern utilizing a model-based decomposition process.
11. The computer readable storage medium according to claim 8, wherein said first optical proximity correction process and said second optical proximity correction process are the same processes.
12. The computer readable storage medium according to claim 11, wherein said first optical proximity correction process and said second optical proximity correction utilizes a rule-based correction process.
13. The computer readable storage medium according to claim 11, wherein said first optical proximity correction process and said second optical proximity correction utilize a model-based correction process.
14. The computer readable storage medium according to claim 8, wherein said first error and said second error are determined in said stitching areas associated with said first pattern and said second pattern.
15. A device manufacturing method comprising the steps of:
(a) providing a substrate that is at least partially covered by a layer of radiation-sensitive material;
(b) providing a projection beam of radiation using an imaging system;
(c) using patterns on masks to endow the projection beam with patterns in its cross-section;
(d) projecting the patterned beam of radiation onto a target portion of the layer of radiation-sensitive material,
wherein in step (c), providing a pattern on a mask includes the steps of:
separating features to be printed into a first pattern and a second pattern;
performing a first optical proximity correction process on said first pattern and said second pattern;
determining an imaging performance of said first pattern and said second pattern;
determining a first error between said first pattern and said imaging performance of said first pattern, and a second error between said second pattern and said imaging performance of said second pattern;
utilizing said first error to adjust stitching areas associated with said first pattern to generate a modified first pattern;
utilizing said second error to adjust stitching areas associated with said second pattern to generate a modified second pattern; and
applying a second optical proximity correction process to said modified first pattern and said modified second pattern.
16. A method for generating masks to be utilized in a photolithography process, said method comprising the steps of:
decomposing a target circuit pattern containing features to be printed on a wafer, into multiple patterns, by separating said features to be printed into a first pattern and a second pattern;
performing a first optical proximity correction process on said first pattern and said second pattern;
determining an imaging performance of said first pattern and said second pattern;
determining a first error between said first pattern and said imaging performance of said first pattern, and a second error between said second pattern and said imaging performance of said second pattern;
utilizing said first error to adjust stitching areas associated with said first pattern to generate a modified first pattern;
utilizing said second error to adjust stitching areas associated with said second pattern to generate a modified second pattern;
applying a second optical proximity correction process to said modified first pattern and said modified second pattern; and
generating a first mask corresponding to said modified first pattern after said second optical proximity correction process, and a second mask corresponding to said modified second pattern after said second optical proximity correction process.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A computer-readable recording medium storing a computer-executable program which, when executed by a processor, performs a method for determining how one or more human capital practices of an organization create an impact on business results of the organization, the method comprising:
a. generating a human capital process tier, a human capital capabilities tier having a set of human capital capabilities evaluation elements, a key performance drivers tier, and a business results tier; said
i. human capital process tier comprising: a set of human capital process evaluation elements for analyzing an approach of the organization to a human capital process; a set of survey questions regarding each of the human capital process evaluation elements; a maturity score determination algorithm containing instructions for causing the computer executing the algorithm to combine and weigh data from the survey questions to determine a maturity score of each of the human capital process evaluation elements; and a capital process algorithm containing instructions for causing the computer executing the algorithm to statistically link a maturity score for each of the human capital process evaluation elements of the capital process tier to human capital capabilities evaluation elements of the human capital capabilities tier using a regression analysis;
ii. human capital capabilities tier comprising: a set of human capital capabilities evaluation elements for evaluating employee attitudes and abilities; a set of survey questions regarding each of the human capital evaluation elements; a capital capabilities score determination algorithm containing instructions for causing the computer executing the algorithm to compile and determine a score that assesses strengths and weaknesses of each of the human capital capabilities evaluation elements; and a capital capabilities algorithm containing instructions for causing the computer executing the algorithm to statistically link the score of each of the human capital capabilities evaluation elements to key performance drivers elements of the key performance tier using a regression analysis;
iii. key performance drivers tier comprising: a set of key performance drivers elements for evaluating non-financial intermediate organization outcomes; a set of survey questions regarding each of the key performance drivers elements; a key performance drivers score determination algorithm containing instructions for causing the computer executing the algorithm to compile and determine a score that assesses strengths and weaknesses of each of the key performance drivers elements; and a key performance drivers algorithm containing instructions causing the computer executing the algorithm to statistically link the score of each of the key performance driving elements to business results elements of the business results tier using a regression analysis; and
iv. business results tier comprising: a set of business results elements for measuring business results; a set of survey questions regarding each of the business results elements; a business results score determination algorithm containing instructions for causing the computer executing the algorithm to compile and determine a score that assesses strengths and weaknesses of each of the business results elements;
b. generating a score set by performing statistical analysis on each of the elements of the human capital process tier, human capital capabilities tier, key performance drivers tier, and business results tier;
c. generating a scorecard for illustrating the impact of the one or more human capital practices by processing the score set; and
d. using the scorecard for assessing how the organization’s human capital practices impact its business results.
2. The computer-readable recording medium of claim 1 wherein the step of generating a scorecard comprises the steps of:
developing a set of numeric and graphic terms suitable for benchmarking; and
generating a fact-based foundation for marking recommendations for development and management of personnel for improving business results.
3. The computer-readable recording medium of claim 2, wherein the step of generating a scorecard comprises the steps of:
processing the set of numeric and graphic terms, and
processing the fact-based foundation to generate a scorecard for illustrating the impact of the human capital process.
4. The computer-readable recording medium of claim 1 wherein the set of human capital process evaluation elements comprises one or more elements selected from a group consisting of:
a competency management element, a career development element, a performance appraisal element, a succession planning element, a recruiting element, a workforce planning element, a workforce design element, a rewards and recognition element, an employee relations element, a human capital strategy element, a learning management element, a knowledge management element, and a human capital infrastructure element.
5. The computer-readable recording medium of claim 1 wherein the set of questions on the human capital process elements comprises one or more questions selected from a group consisting of questions on:
best practice activities supported by the process; technology supporting the process, skills and abilities necessary for people to effectively carryout the process; organizational commitment to the process; improvement of the process; or the process’s effectiveness in supporting employees.
6. The computer-readable recording medium of claim 1 wherein the set of human capital capabilities evaluation elements comprises one or more elements selected from a group consisting of:
a leadership element, a workforce proficiency element, a workforce adaptability element, and a human capital efficiency element.
7. The computer-readable recording medium of claim 1 wherein the set of key performance drivers elements comprises one or more elements selected from a group consisting of:
a productivity element, a quality element, an innovation element, and a customers element.
8. The computer-readable recording medium of claim 1 wherein the set of business results elements comprises one or more elements selected from a group consisting of:
a revenue growth element, a market share element, and a stock performance element.
9. The computer-readable recording medium of claim 1 wherein the impact of the human capital practice comprises organizational strengths in various elements, areas for improvement, and an assessment of process maturity in human capital capabilities.
10. A computer-implemented method for determining how an organization’s human capital practices impact its business results; the computer including a processor and memory and the method comprising steps performed by the computer of:
a. generating, by the processor, a human capital process tier, a human capital capabilities tier having a set of human capital capabilities evaluation elements, a key performance drivers tier, and a business results tier; said
i. human capital process tier comprising: a set of human capital process evaluation elements for analyzing an approach of the organization to a human capital process; a set of survey questions regarding each of the human capital process evaluation elements; a maturity score determination algorithm containing instructions for causing the computer executing the algorithm to combine and weigh data from the survey questions to determine a maturity score of each of the human capital process evaluation elements; and a capital process algorithm containing instructions for causing the computer executing the algorithm to statistically link a maturity score for each of the human capital process evaluation elements of the capital process tier to human capital capabilities evaluation elements of the human capital capabilities tier using a regression analysis;
ii. human capital capabilities tier comprising: a set of human capital capabilities evaluation elements for evaluating employee attitudes and abilities; a set of survey questions regarding each of the human capital evaluation elements; a capital capabilities score determination algorithm containing instructions for causing the computer executing the algorithm to compile and determine a score that assesses strengths and weaknesses of each of the human capital capabilities evaluation elements; and a capital capabilities algorithm containing instructions for causing the computer executing the algorithm to statistically link the score of each of the human capital capabilities evaluation elements to key performance drivers elements of the key performance tier using a regression analysis;
iii. key performance drivers tier comprising: a set of key performance drivers elements for evaluating non-financial intermediate organization outcomes; a set of survey questions regarding each of the key performance drivers elements; a key performance drivers score determination algorithm containing instructions for causing the computer executing the algorithm to compile and determine a score that assesses strengths and weaknesses of each of the key performance drivers elements; and a key performance drivers algorithm containing instructions causing the computer executing the algorithm to statistically link the score of each of the key performance driving elements to business results elements of the business results tier using a regression analysis; and
iv. business results tier comprising: a set of business results elements for measuring business results; a set of survey questions regarding each of the business results elements; a business results score determination algorithm containing instructions for causing the computer executing the algorithm to compile and determine a score that assesses strengths and weaknesses of each of the business results elements;
b. generating, by the processor, a score set by performing statistical analysis on each of the elements of the human capital process tier, human capital capabilities tier, key performance drivers tier, and business results tier;
c. generating, by the processor, a scorecard for illustrating the impact of the one or more human capital practices by processing the score set; and
d. using the scorecard for assessing how the organization’s human capital practices impact its business results.
11. The method of claim 10 wherein the step of generating a scorecard comprises the steps of:
developing a set of numeric and graphic terms suitable for benchmarking; and
generating a fact-based foundation for marking recommendations for development and management of personnel for improving business results.
12. The method of claim 11, wherein the step of generating a scorecard comprises the steps of:
processing the set of numeric and graphic terms, and
processing the fact-based foundation to generate a scorecard for illustrating the impact of the human capital process.
13. The method of claim 10 wherein the set of human capital process evaluation elements comprises one or more elements selected from a group consisting of:
a competency management element, a career development element, a performance appraisal element, a succession planning element, a recruiting element, a workforce planning element, a workforce design element, a rewards and recognition element, an employee relations element, a human capital strategy element, a learning management element, a knowledge management element, and a human capital infrastructure element.
14. The method of claim 10 wherein the set of questions on the human capital process elements comprises one or more questions selected from a group consisting of questions on:
best practice activities supported by the process; technology supporting the process, skills and abilities necessary for people to effectively carryout the process; organizational commitment to the process; improvement of the process; or the process’s effectiveness in supporting employees.
15. The method of claim 10 wherein the set of human capital capabilities evaluation elements comprises one or more elements selected from a group consisting of:
a leadership element, a workforce proficiency element, a workforce adaptability element, and a human capital efficiency element.
16. The method of claim 10 wherein the set of key performance drivers elements comprises one or more elements selected from a group consisting of:
a productivity element, a quality element, an innovation element, and a customers element.
17. The method of claim 10 wherein the set of business results elements comprises one or more elements selected from a group consisting of:
a revenue growth element, a market share element, and a stock performance element.
18. The method of claim 10 wherein the impact of the human capital practice comprises organizational strengths in various elements, areas for improvement, and an assessment of process maturity in human capital capabilities.