Measurement method and apparatus for semiconductor features with increased throughput

Abstract

A system and a method for measuring of parameter values of semiconductor objects within wafers with increased throughput include using a modified machine learning algorithm to extract measurement results from instances of semiconductor objects. A training method for training the modified machine learning algorithm includes reducing a user interaction. The method can be more flexible and robust and can involve less user interaction than conventional methods. The system and method can be used for quantitative metrology of integrated circuits within semiconductor wafers.

What is claimed is: 
     
         1 . A method of generating training data for training a contiguous machine learning algorithm for providing quantitative measurement results of a parameter of a semiconductor object of interest from cross section images generated by a charged particle beam system, the method comprising:
 selecting a parametrized description of the semiconductor object of interest, the parametrized description comprising the parameter; and   generating a set of training cross section image segments of the semiconductor object of interest, the training cross section image segments comprising a variation of a parameter value of the semiconductor object of interest,   wherein the variation of the parameter value is within a selected parameter value range.   
     
     
         2 . The method according to  claim 1 , further comprising receiving the selected parameter value range via a user input. 
     
     
         3 . The method according to  claim 1 , wherein the parameter of the parametrized description of the semiconductor object of interest comprises a member selected from the group consisting of a dimension, a length, a diameter, a distance, an area, an angle, a radius, an ellipticity, an aspect ratio, a curvature, a periodicity, and a polygon parameter. 
     
     
         4 . The method according to  claim 1 , wherein selecting the parametrized description comprises:
 using a user interface to present a plurality of parametrized descriptions for selection; and   using user input to configure the selected parametrized description.   
     
     
         5 . The method according to  claim 1 , further comprising receiving imaging parameters of the charged particle beam system, wherein the imaging parameters comprise at least one member selected from the group consisting of a resolution, a contrast, a noise level, a point spread function, a dwell time, a contrast method, a material contrast, and a topography contrast. 
     
     
         6 . The method according to  claim 1 , wherein:
 generating the set of training cross-section image segments comprises annotating a plurality of cross section images comprising the semiconductor object of interest with the at least one annotation value; and   the annotation value represents a measurement result of the parameter value of an instance of the semiconductor object of interest.   
     
     
         7 . The method according to  claim 6 , further comprising automatically detecting, based on the selected parametrized description, instances of the semiconductor object of interest in the set of training cross section image segments. 
     
     
         8 . The method according to  claim 7 , further comprising automatically determining an initial annotation value for each detected instance of the semiconductor object of interest in the first set of training cross section image segments. 
     
     
         9 . The method according to  claim 8 , wherein automatically determining the initial annotation value comprises applying a physical simulation model to a parametric description of the semiconductor object of interest and determining the parameter values of the parametric description by optimization. 
     
     
         10 . The method according to  claim 8 , further comprising:
 graphically presenting, at at least one detected instance of the semiconductor object of interest within the set of training cross section image segments, the parametrized description with the initial annotation value via a user interface; and   receiving, via a user input, a confirmation or refinement of the initial annotation value.   
     
     
         11 . The method according to  claim 1 , wherein generating the set of training cross section image segments comprises:
 receiving at least a first set of training cross section image segments of the semiconductor object of interest from the charged particle beam system, the first set of training cross section image segments covering a first parameter value range; and   using image processing to generate from the first set of training cross section image segments a second set of training cross-section image segments of the semiconductor object of interest within a second parameter range,   wherein image processing comprises at least member selected from the group consisting of a variation of a scale, a change of a shape, an interpolation, a morphologic operation, and a pattern substitution.   
     
     
         12 . The method according to  claim 1 , wherein generating the set of training cross section image segments comprises physically simulating cross section image segments based on a-priori information of the semiconductor object of interest and imaging parameters of the charged particle beam system. 
     
     
         13 . The method according to  claim 12 , wherein the physical simulation comprises:
 receiving CAD data of the semiconductor object of interest;   selecting the parametrized description of the semiconductor object of interest according the CAD data;   varying the CAD data according the selected parameter value range of the parametrized description; and   physically simulating the imaging with the charged particle beam system to obtain the set of training cross section image segments.   
     
     
         14 . The method according to  claim 1 , wherein the training data is configured to train a machine learning algorithm to provide continuous quantitative measurement of the parameter of the semiconductor object of interest. 
     
     
         15 . One or more machine-readable hardware storage devices comprising instructions that are executable by one or more processing devices to perform operations comprising the method of  claim 1 . 
     
     
         16 . A system comprising:
 one or more processing devices; and   one or more machine-readable hardware storage devices comprising instructions that are executable by the one or more processing devices to perform operations comprising the method of  claim 1 .   
     
     
         17 . A method of performing measurements of semiconductor objects within a wafer, the method comprising:
 obtaining at least one digital 2D cross section image slice comprising at least one cross section of a semiconductor object of interest; and   determining at least one quantitative measurement result of at least one predefined parameter of the semiconductor object of interest by a contiguous machine learning algorithm directly applied to the digital 2D cross section image slice,   wherein the at least one predefined parameter comprises a parameter of a parametrized geometrical description of the semiconductor object of interest.   
     
     
         18 . The method according to  claim 17 , wherein the at least one predefined parameter of the parametrized geometrical description comprises a member selected from the group consisting of a length, a diameter, a distance, an area, an angle, a radius, an ellipticity, an aspect ratio, a curvature, a periodicity, and a polygon parameter. 
     
     
         19 . One or more machine-readable hardware storage devices comprising instructions that are executable by one or more processing devices to perform operations comprising the method of  claim 17 . 
     
     
         20 . A system comprising:
 a charged particle beam system comprising at least one charged particle beam column configured to obtain at least one 2D cross section image slice;   one or more processing devices; and   one or more machine-readable hardware storage devices comprising instructions that are executable by the one or more processing devices to perform operations comprising the method of  claim 17 .