Patent Number: 
Section: claims

1. A method for semiconductor device production process monitoring, the method comprising the steps of:during the production process of the semiconductor device, obtaining a scanning electron microscopy (SEM) image of a target pattern formed on a sample through a predetermined process;obtaining a feature amount of the obtained SEM image of the target pattern;estimating a cross-sectional shape of the target pattern by comparing (a) the obtained feature amount of the obtained SEM image of the target pattern to (b) a database that preliminarily stores a first learning data representing correlations between (1) data of feature amounts of images obtained from SEM images obtained from various three-dimensional (3D) shape patterns, and (2) data obtained by measurement of cross-sectional shapes of the 3D shape patterns;estimating a process condition of the target pattern by comparing (a) the obtained feature amount of the SEM image of the target pattern to (c) a database that preliminarily stores a second learning data representing correlations between (1) data of feature amounts of images obtained from SEM images obtained from various 3D shape patterns, and (3) data of process conditions when the various 3D shape patterns are formed;determining whether the estimated cross-sectional shape of the target pattern satisfies a preliminarily set specification through comparison to the specification, and if the estimated cross-sectional shape of the target pattern does not satisfy the preliminarily set specification, calculating a process condition adjustment amount by using the estimated process condition of the target pattern;wherein the process condition adjustment amount provides feedback to the production process of the semiconductor device. 2. A method for semiconductor device production process monitoring, according to claim 1, wherein:the data of process conditions are stored into the database by being correlated to data obtained by measurement of cross-sectional shapes of the 3D shape and process conditions applied when the 3D shape pattern is formed. 3. A method for semiconductor device production process monitoring, according to claim 1, wherein the process conditions for forming the 3D shape pattern include at least one of focus positions, dose amounts, exposure time, baking temperatures, baking time in an exposure apparatus, intra-chamber temperatures, gas flow volumes, and gas pressure in an etching apparatus. 4. A method for semiconductor device production process monitoring, according to claim 1, wherein:the sample is monitored after an exposure step; andthe method further comprises a step of providing feedback to the exposure step. 5. A method for semiconductor device production process monitoring, according to claim 1, wherein:the sample is monitored after an etching step; andthe method further comprises a step of providing feedback to the etching step. 6. A method for semiconductor device production process monitoring, according to claim 1,wherein the step of estimating the cross-sectional shape of the target pattern includes the steps of:displaying a distribution of the data of feature amounts of images obtained from SEM images obtained from various three-dimensional shape patterns and the data obtained by measurement of cross-sectional shapes of the 3D shape patterns;selecting a estimation engine from a plurality of estimation engines to be used for estimating the cross-sectional shape of the target pattern by using the distribution. 7. A method for semiconductor device production process monitoring, according to claim 1,wherein the feature amount of the obtained SEM image of the target pattern is a texture feature amount. 8. A method for semiconductor device production process monitoring, according to claim 1,wherein the feature amount of the obtained SEM image of the target pattern is a differential line width feature amount. 9. An apparatus for semiconductor device production process monitoring, the apparatus comprising:an SEM image obtaining unit that, during the production process of the semiconductor device, obtains an SEM image of a target pattern formed on a sample through a predetermined process;a feature amount calculation unit that obtains a feature amount of the SEM image of the target pattern that has been obtained by the SEM image obtaining unit;a database unit that stores (1) data of feature amounts of images obtained from SEM images obtained from various 3D shape patterns, (2) data obtained by measurement of cross-sectional shapes of the 3D shape patterns to have correlation to one another, (3) data of process conditions when the various 3D shape patterns are formed, and data representing correlations between at least two of the other types of data;an estimation model comparison unit that adapts the feature amount obtained by the feature amount calculation unit to an estimation model determined through comparison to the data of the image feature amounts of the SEM images of the various 3D shape patterns, the data of the image feature amounts being preliminarily stored in the database so as to have correlation to the data obtained by measurement of the cross-sectional shapes of the 3D shape patterns;a cross-sectional shape estimation unit that estimates a cross-sectional shape of the target pattern in accordance with the estimation model to which the feature amount is adapted by the estimation model comparison unit;a determining unit that determines whether the cross-sectional shape of the target pattern, which is estimated by the cross-sectional shape estimation unit, satisfies a preliminarily set specification through comparison to the specification, and if the estimated cross-sectional shape of the target pattern does not satisfy the preliminarily set specification, estimating a process condition of the target pattern by comparing the obtained feature amount of the SEM image of the target pattern to the database unit that stores data representing correlations between the (1) data of feature amounts of images obtained from SEM images obtained from various 3D shape patterns, and the (3) data of process conditions when the various 3D shape patterns are formed; anda process condition adjustment amount calculating unit that calculates a process condition adjustment amount, and that uses the estimated process condition of the target pattern for providing feedback to the production process of the semiconductor device. 10. An apparatus for semiconductor device production process monitoring, according to claim 9, wherein:the various 3D shape patterns to be stored into the database unit to have correlation to the data obtained by measurement of the cross-sectional shape are formed by varying process conditions for forming the 3D shape patterns; andthe feature amount of the SEM image of the target pattern is stored into the database by being correlated to data obtained by measurement of the cross-sectional shape of the 3D shape and process conditions applied when the 3D shape pattern is formed. 11. An apparatus for semiconductor device production process monitoring, according to claim 10, wherein the process conditions for forming the 3D shape pattern include at least of focus positions, dose amounts, exposure time, baking temperatures, baking time in an exposure apparatus, intra-chamber temperatures, gas flow volumes, and gas pressure in an etching apparatus. 12. An apparatus for semiconductor device production process monitoring, according to claim 9, wherein:the predetermined process is an exposure step; andthe apparatus further comprises a feedback unit that feeds back a result of determination made by the determining unit to the exposure step. 13. An apparatus for semiconductor device production process monitoring, according to claim 9, wherein:the predetermined process is an etching step; andthe apparatus further comprises feed back unit that feeds back a result of determination made by the determining unit to at least one of an exposure step which is performed prior to the etching step, and the etching step. 14. A method for estimating a cross sectional shape of a pattern, the method comprising the steps of:obtaining an SEM image of a target pattern formed on a sample;obtaining a feature amount of the obtained SEM image of the target pattern;estimating a cross-sectional shape of the target pattern by comparing (a) the obtained feature amount of the obtained SEM image of the target pattern to (b) a database that preliminarily stores learning data representing correlations between (1) data of feature amounts of images obtained from SEM images obtained from various 3D shape patterns, (2) data obtained by measurement of cross-sectional shapes of the 3D shape patterns, and (3) data of process conditions when the various 3D shape patterns are formed;estimating a cross-sectional shape of the target pattern in accordance with the estimation model to which the feature amount is adapted, and if said estimated cross-sectional shape of the target pattern does not satisfy a preliminarily set specification, estimating a process condition of the target pattern by comparing the obtained feature amount of the SEM image of the target pattern to correlation data stored in said database unit, said correlation data representing correlations between the (1) data of feature amounts of images obtained from SEM images obtained from various 3D shape patterns, and the (3) data of process conditions when the various 3D shape patterns are formed; andcalculating a process condition adjustment amount, that uses the estimated process condition of the target pattern for providing feedback to the production process of the semiconductor device. 15. A method for estimating a cross sectional shape of a pattern, according to claim 14, wherein:in the step of adapting the feature amount to the estimation model, the various 3D shape patterns are formed by varying process conditions for forming the 3D shape patterns; andthe feature amount of the SEM image of the target pattern is stored into the database by being correlated to data obtained by measurement of the cross-sectional shape of the 3D shape and process conditions applied when the 3D shape pattern is formed. 16. A method for estimating a cross sectional shape of a pattern, according to claim 14, wherein, in the step of adapting the feature amount to the estimation model, the feature amount of the SEM image of the target pattern is stored into the database by being correlated to data obtained by measurement of the cross-sectional shape of the 3D shape and device characteristics of a circuit formed by using the 3D shape pattern. 17. A method for estimating a cross sectional shape of a pattern, according to claim 14, wherein:in the step of adapting the feature amount to the estimation model, a plurality of candidates respectively including feature amounts of images that are similar to the feature amount of the SEM image of the target pattern are extracted; andthe cross-sectional shape of the 3D shape is estimated by using information of the feature amounts of the images of the plurality of candidates extracted. 18. A method for estimating a cross sectional shape of a pattern, according to claim 14, wherein the cross-sectional shape of the 3D shape pattern is estimated by using an estimation engine selected from a plurality of estimation engines. 19. A method for estimating a cross sectional shape of a pattern, according to claim 14, wherein, in the step of obtaining the SEM image of the target pattern, a tilt SEM image is obtained from the target pattern observed from a tilt direction. 20. An apparatus for estimating a cross sectional shape of a pattern, the apparatus comprising:an SEM image obtaining unit that obtains an SEM image of a target pattern formed on a sample;a database unit that stores (1) data of feature amounts of images obtained from SEM images obtained from various 3D shape patterns, (2) data obtained by measurement of cross-sectional shapes of the 3D shape patterns to have correlation to one another, and (3) data of process conditions when the various 3D shape patterns are formed;an estimation model comparison unit that adapts the feature amount obtained by a feature amount calculation unit to an estimation model determined through comparison to the data of the image feature amounts of the SEM images of the various 3D shape patterns, the data of the image feature amounts being preliminarily stored in the database so as to have correlation to the data obtained by measurement of the cross-sectional shapes of the 3D shape patterns; anda cross-sectional shape estimation unit that estimates a cross-sectional shape of the target pattern in accordance with the estimation model to which the feature amount is adapted by the estimation model comparison unit; anda determining unit that determines whether the cross-sectional shape of the target pattern satisfies a preliminarily set specification, and if the estimated cross-sectional shape of the target pattern does not satisfy the preliminarily set specification, estimating a process condition of the target pattern by comparing the obtained feature amount of the SEM image of the target pattern to the database unit that stores data representing correlations between the (1) data of feature amounts of images obtained from SEM images obtained from various 3D shape patterns, and the (3) data of process conditions when the various 3D shape patterns are formed; anda process condition adjustment amount calculating unit that calculates a process condition adjustment amount, and that uses the estimated process condition of the target pattern for providing feedback to the production process of the semiconductor device. 21. An apparatus for estimating a cross sectional shape of a pattern, according to claim 20, wherein:the various 3D shape patterns to be stored into the database unit to have correlation to the data obtained by measurement of the cross-sectional shape are formed by varying process conditions for forming the 3D shape patterns; andthe feature amount of the SEM image of the target pattern is stored into the database by being correlated to data obtained by measurement of the cross-sectional shape of the 3D shape and process conditions applied when the 3D shape pattern is formed. 22. An apparatus for estimating a cross sectional shape of a pattern, according to claim 20, wherein the feature amount of the SEM image of the target pattern is stored into the database by being correlated to data obtained by measurement of the cross-sectional shape of the 3D shape and process conditions applied when the 3D shape pattern is formed. 23. An apparatus for estimating a cross sectional shape of a pattern, according to claim 20, wherein:the estimation model comparison unit extracts a plurality of candidates respectively including feature amounts of images that are similar to the feature amount of the SEM image of the target pattern are extracted; andthe cross-sectional shape estimation unit obtains the cross-sectional shape of the 3D by interpolating the feature amounts of the plurality of candidates extracted. 24. An apparatus for estimating a cross sectional shape of a pattern, according to claim 23, wherein the estimation model comparison unit comprises a plurality of estimation engines for estimating the cross-sectional shape of the 3D shape pattern and estimates the cross-sectional shape of the 3D shape pattern by using an estimation engine selected from the plurality of estimation engines. 25. An apparatus for estimating a cross sectional shape of a pattern, according to claim 20, wherein the SEM image obtaining unit obtains a tilt SEM image from the target pattern observed from a tilt direction. 26. An apparatus for estimating a cross sectional shape of a pattern, according to claim 20, the apparatus further comprising display unit that displays on a screen the cross-sectional shape of the target pattern in an arrangement with measurement values taken of a plurality of dimensional measurement portions of the cross-sectional shape. 27. A method for semiconductor device production process monitoring, the method comprising the steps of:during the production process of the semiconductor device, obtaining an scanning electron microscopy (SEM) image of a target pattern formed on a sample through a predetermined process;obtaining a feature amount of the obtained SEM image of the target pattern;estimating a cross-sectional shape of the target pattern by comparing (a) the obtained feature amount of the SEM image of the target pattern to (b) a database that preliminarily stores a first learning data representing correlations between (1) data of feature amounts of images obtained from SEM images obtained from various three-dimensional (3D) shape patterns, and (2) data obtained by measurement of cross-sectional shapes of the 3D shape patterns;determining whether the estimated cross-sectional shape of the target pattern satisfies a preliminarily set specification through comparison to the specification, and if the estimated cross-sectional shape of the target pattern does not satisfy the preliminarily set specification, estimating a process condition of the target pattern by comparing (a) the obtained feature amount of the SEM image of the target pattern to (c) a database that preliminarily stores a second learning data representing correlations between (1) data of feature amounts of images obtained from SEM images obtained from various 3D shape patterns, and (3) data of process conditions when the various 3D shape patterns are formed;calculating a process condition adjustment amount by using the estimated process condition of the target pattern, the process condition adjustment amount being used for providing feedback to the production process of the semiconductor device.