US20040267397A1 - Optical metrology of structures formed on semiconductor wafer using machine learning systems - Google Patents

Optical metrology of structures formed on semiconductor wafer using machine learning systems Download PDF

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Publication number
US20040267397A1
US20040267397A1 US10/608,300 US60830003A US2004267397A1 US 20040267397 A1 US20040267397 A1 US 20040267397A1 US 60830003 A US60830003 A US 60830003A US 2004267397 A1 US2004267397 A1 US 2004267397A1
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machine learning
learning system
diffraction
diffraction signal
training
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Srinivas Doddi
Emmanuel Drege
Nickhil Jakatdar
Junwei Bao
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Tokyo Electron Ltd
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TEL Timbre Technologies Inc
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Priority to US10/608,300 priority Critical patent/US20040267397A1/en
Assigned to TIMBRE TECHNOLOGIES, INC. reassignment TIMBRE TECHNOLOGIES, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BAO, JUNWEI, DODDI, SRINIVAS, DREGE, EMMANUEL, JAKATDAR, NICKHIL
Priority to PCT/US2004/020682 priority patent/WO2005003911A2/en
Priority to KR1020057024949A priority patent/KR101059427B1/ko
Priority to DE112004001001T priority patent/DE112004001001T5/de
Priority to CNB2004800149754A priority patent/CN100418083C/zh
Priority to JP2006517724A priority patent/JP4589315B2/ja
Publication of US20040267397A1 publication Critical patent/US20040267397A1/en
Priority to US12/399,011 priority patent/US7831528B2/en
Assigned to TOKYO ELECTRON LIMITED reassignment TOKYO ELECTRON LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: TIMBRE TECHNOLOGIES, INC.
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor

Definitions

  • the present invention relates to metrology of structures formed on semiconductor wafers, and more particularly to metrology of structures formed on semiconductor wafers using machine learning systems.
  • Optical metrology involves directing an incident beam at a structure, measuring the resulting diffracted beam, and analyzing the diffracted beam to determine a feature of the structure.
  • optical metrology is typically used for quality assurance. For example, after fabricating a periodic grating in proximity to a semiconductor chip on a semiconductor wafer, an optical metrology system is used to determine the profile of the periodic grating. By determining the profile of the periodic grating, the quality of the fabrication process utilized to form the periodic grating, and by extension the semiconductor chip proximate the periodic grating, can be evaluated.
  • One conventional optical metrology system uses a diffraction modeling technique, such as rigorous coupled wave analysis (RCWA), to analyze the diffracted beam. More particularly, in the diffraction modeling technique, a model diffraction signal is calculated based, in part, on solving Maxwell's equations. Calculating the model diffraction signal involves performing a large number of complex calculations, which can be time consuming and costly.
  • RCWA rigorous coupled wave analysis
  • a structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device.
  • a second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal.
  • the first and second diffraction signals are compared.
  • a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.
  • FIG. 1 depicts an exemplary optical metrology system
  • FIGS. 2A-2E depict exemplary profiles
  • FIG. 3 depicts an exemplary neural network
  • FIG. 4 depicts an exemplary process of training a machine learning system
  • FIG. 5 depicts an exemplary process of testing a machine learning system
  • FIG. 6 depicts an exemplary process of determining a feature of a structure using a machine learning system
  • FIG. 7 depicts an exemplary process of determining a feature of a structure using a machine learning system in a library-based process
  • FIG. 8 depicts an exemplary system to determine a feature of a structure using a machine learning system in a library-based system
  • FIG. 9 depicts an exemplary process of determining a feature of a structure using a machine learning system in a regression-based process
  • FIG. 10 depicts an exemplary system to determine a feature of a structure using a machine learning system in a regression-based system.
  • a metrology system 100 can be used to examine and analyze a structure.
  • metrology system 100 can be used to determine a feature of a periodic grating 102 formed on wafer 104 .
  • periodic grating 102 can be formed in test areas on wafer 104 , such as adjacent to a device formed on wafer 104 .
  • periodic grating 102 can be formed in an area of the device that does not interfere with the operation of the device or along scribe lines on wafer 104 .
  • metrology system 100 can include a metrology device with a source 106 and a detector 112 .
  • Periodic grating 102 is illuminated by an incident beam 108 from source 106 .
  • incident beam 108 is directed onto periodic grating 102 at an angle of incidence ⁇ i with respect to normal n of periodic grating 102 and an azimuth angle ⁇ (i.e., the angle between the plane of incidence beam 108 and the direction of the periodicity of periodic grating 102 ).
  • Diffracted beam 110 leaves at an angle of ⁇ d with respect to normal ⁇ right arrow over (n) ⁇ and is received by detector 112 .
  • Detector 112 converts the diffracted beam 110 into a measured diffraction signal, which can include reflectance, tan ( ⁇ ), cos ( ⁇ ), Fourier coefficients, and the like.
  • Metrology system 100 also includes a processing module 114 configured to receive the measured diffraction signal and analyze the measured diffraction signal. As described below, a feature of periodic grating 102 can then be determined using a library-based process or a regression-based process. Additionally, other linear or non-linear profile extraction techniques are contemplated.
  • the measured diffraction signal is compared to a library of diffraction signals. More specifically, each diffraction signal in the library is associated with a profile of the structure.
  • each diffraction signal in the library is associated with a profile of the structure.
  • the profile associated with the matching diffraction signal in the library is presumed to represent the actual profile of the structure.
  • a feature of the structure can then be determined based on the profile associated with the matching diffraction signal.
  • processing module 114 compares the measured diffraction signal to diffraction signals stored in a library 116 .
  • Each diffraction signal in library 116 is associated with a profile.
  • the profile associated with the matching diffraction signal in library 116 can be presumed to represent the actual profile of periodic grating 102 .
  • the set of profiles stored in library 116 can be generated by characterizing a profile using a set of parameters, then varying the set of parameters to generate profiles of varying shapes and dimensions.
  • the process of characterizing a profile using a set of parameters can be referred to as parameterizing.
  • profile 200 can be characterized by parameters h1 and w1 that define its height and width, respectively.
  • additional shapes and features of profile 200 can be characterized by increasing the number of parameters.
  • profile 200 can be characterized by parameters h1, w1, and w2 that define its height, bottom width, and top width, respectively.
  • the width of profile 200 can be referred to as the critical dimension (CD).
  • parameter w1 and w2 can be described as defining the bottom CD and top CD, respectively, of profile 200 .
  • various types of parameters can be used to characterize profile 200 , including angle of incident (AOI), pitch, n & k, hardware parameters (e.g., polarizer angle), and the like.
  • the set of profiles stored in library 116 can be generated by varying the parameters that characterize the profile. For example, with reference to FIG. 2B, by varying parameters h1, w1, and w2, profiles of varying shapes and dimensions can be generated. Note that one, two, or all three parameters can be varied relative to one another.
  • the parameters of the profile associated with a matching diffraction signal can be used to determine a feature of the structure being examined.
  • a parameter of the profile corresponding to a bottom CD can be used to determine the bottom CD of the structure being examined.
  • the number of profiles and corresponding diffraction signals in the set of profiles and diffraction signals stored in library 116 depends, in part, on the range over which the set of parameters and the increment at which the set of parameters are varied.
  • the profiles and the diffraction signals stored in library 116 are generated prior to obtaining a measured diffraction signal from an actual structure.
  • the range and increment (i.e., the range and resolution) used in generating library 116 can be selected based on familiarity with the fabrication process for a structure and what the range of variance is likely to be.
  • the range and/or resolution of library 116 can also be selected based on empirical measures, such as measurements using atomic force microscopy (AFM), scanning electron microscopy (SEM), and the like.
  • the measured diffraction signal is compared to a diffraction signal generated prior to the comparison (i.e., a trial diffraction signal) using a set of parameters (i.e., trial parameters) for a profile. If the measured diffraction signal and the trial diffraction signal do not match or when the difference of the measured diffraction signal and the trial diffraction signal is not within a preset or matching criterion, another trial diffraction signal is generated using another set of parameters for another profile, then the measured diffraction signal and the newly generated trial diffraction signal are compared.
  • a diffraction signal generated prior to the comparison i.e., a trial diffraction signal
  • trial parameters i.e., trial parameters
  • the profile associated with the matching trial diffraction signal is presumed to represent the actual profile of the structure.
  • the profile associated with the matching trail diffraction signal can then be used to determine a feature of the structure being examined.
  • processing module 114 can generate a trial diffraction signal for a profile, and then compare the measured diffraction signal to the trial diffraction signal. As described above, if the measured diffraction signal and the trial diffraction signal do not match or when the difference of the measured diffraction signal the trial diffraction signals is not within a preset or matching criterion, then processing module 114 can iteratively generate another trial diffraction signal for another profile.
  • the subsequently generated trial diffraction signal can be generated using an optimization algorithm, such as global optimization techniques, which includes simulated annealing, and local optimization techniques, which includes steepest descent algorithm.
  • the trial diffraction signals and profiles can be stored in a library 116 (i.e., a dynamic library).
  • the trial diffraction signals and profiles stored in library 116 can then be subsequently used in matching the measured diffraction signal.
  • library 116 can be omitted from metrology system 100 .
  • diffraction signals used in a library-based process and/or a regression-based process are generated using a machine learning system 118 employing a machine learning algorithm, such as back-propagation, radial basis function, support vector, kernel regression, and the like.
  • a machine learning algorithm such as back-propagation, radial basis function, support vector, kernel regression, and the like.
  • machine learning system 118 receives a profile as an input and generates a diffraction signal as an output.
  • machine learning system 118 is depicted as a component of processing module 114 , it should be recognized that machine learning system 118 can be a separate module.
  • the diffraction signals in library 116 can be generated in advance by machine learning system 118 .
  • machine learning system 118 can be a separate module that is not connected to processing module 114 .
  • machine learning system 118 is used as part of a regression-based process, machine learning system 118 is connected to processing module 114 even when machine learning system 118 is a separate module rather than a component of processing module 114 .
  • the machine learning system is a neural network 300 using a back-propagation algorithm.
  • Neural network 300 includes an input layer 302 , an output layer 304 , and a hidden layer 306 between input layer 302 and output layer 304 .
  • Input layer 302 and hidden layer 306 are connected using links 308 .
  • Hidden layer 306 and output layer 304 are connected using links 310 . It should be recognized, however, that neural network 300 can include any number of layers connected in various configurations.
  • input layer 302 includes one or more input nodes 312 .
  • an input node 312 in input layer 302 corresponds to a parameter of the profile that is inputted into neural network 300 .
  • the number of input nodes 312 corresponds to the number of parameters used to characterize the profile. For example, if a profile is characterized using 2 parameters (e.g., top and bottom widths), input layer 302 includes 2 input nodes 312 , where a first input node 312 corresponds to a first parameter (e.g., a top width) and a second input node 312 corresponds to a second parameter (e.g., a bottom width).
  • output layer 304 includes one or more output nodes 314 .
  • each output node 314 is a linear function. It should be recognized, however, that each output node 314 can be various types of functions.
  • an output node 314 in output layer 304 corresponds to a dimension of the diffraction signal that is outputted from neural network 300 .
  • the number of output nodes 314 corresponds to the number of dimensions used to characterize the diffraction signal.
  • output layer 304 includes 5 output nodes 314 , wherein a first output node 314 corresponds to a first dimension (e.g., a first wavelength), a second output node 314 corresponds to a second dimension (e.g., a second wavelength), etc.
  • a first output node 314 corresponds to a first dimension (e.g., a first wavelength)
  • a second output node 314 corresponds to a second dimension (e.g., a second wavelength)
  • hidden layer 306 includes one or more hidden nodes 316 .
  • each hidden node 316 is a sigmoidal transfer function or a radial basis function. It should be recognized, however, that each hidden node 316 can be various types of functions.
  • the machine learning system Prior to using a machine learning system to generate a diffraction signal, the machine learning system is trained. With reference to FIG. 4, an exemplary process 400 is depicted for training a machine learning system.
  • the machine learning system is trained using a set of training input data and a set of training output data, where an input data in the set of training input data has a corresponding output data in the set of training output data to form an input and an output data pair.
  • the set of training input data is obtained.
  • the training input data includes a set of profiles.
  • a profile is characterized using a set of parameters.
  • a range of profiles can be generated by varying one or more parameters that characterize a profile, either alone or in combination.
  • An overall range of profiles to be generated is determined based on the expected range of variability in the actual profile of the structure to be examined, which is determined either empirically or through experience. For example, if the actual profile of the structure to be examined is expected to have a bottom width that can vary between x 1 and x 2 , then the overall range of profiles can be generated by varying the parameter corresponding to the bottom width between x 1 and x 2 .
  • the set of profiles used to train the machine learning system is selected from the overall range of profiles to be generated. More particularly, the training data set is selected using a random sampling of the overall range of profiles. It should be recognized that various sampling techniques can be used to select the training data set, such as systematic sampling, a combination of random and systematic sampling, and the like.
  • the overall range of profiles to be generated is divided into two or more partitions.
  • a machine learning system is configured and trained for each of the partitions. For example, assume the overall range is divided into a first partition and a second partition. Thus, in this example, a first machine learning system is configured and trained for the first partition, and a second machine learning system is configured and trained for the second partition.
  • One advantage of partitioning the overall range and using multiple machine learning systems is that parallel processing can be used (e.g., the two machine learning systems can be trained and used in parallel).
  • Another advantage is that each of the machine learning systems may be more accurate as to their respective partitions than a single machine learning system for the overall range. More specifically, a single machine learning system trained for the overall range may be susceptible to a local minimum that may reduce the accuracy of the machine learning system.
  • the partitions may be of equal sizes or of varying sizes.
  • the sizes of the partitions can be determined based on the density of the data within the partitions. For example, a less dense partition may be larger than a more dense partition. It should be recognized that the number and size of the partitions can vary depending on the application.
  • the set of training output data is obtained.
  • the training output data includes a set of diffraction signals.
  • a diffraction signal in the set of diffraction signals used as the training output data corresponds to a profile in the set of profiles used as the training input data.
  • Each diffraction signal in the set of diffraction signals can be generated based on each profile in the set of profiles using a modeling technique, such as rigorous coupled wave analysis (RCWA), integral method, Fresnel method, finite analysis, modal analysis, and the like.
  • RCWA rigorous coupled wave analysis
  • each diffraction signal in the set of diffraction signals can be generated based on each profile in the set of profiles using an empirical technique, such as measuring a diffraction signal using a metrology device, such as an ellipsometer, reflectometer, atomic force microscope (AFM), scanning electron microscope (SEM), and the like.
  • a metrology device such as an ellipsometer, reflectometer, atomic force microscope (AFM), scanning electron microscope (SEM), and the like.
  • the set of diffraction signals is transformed using principal component analysis (PCA). More particularly, a diffraction signal can be characterized using a number of dimensions, such as a number of different wavelengths.
  • PCA principal component analysis
  • the diffraction signals are transformed into uncorrelated dimensions, and the space of the uncorrelated dimensions is smaller than the space of the original dimensions. After the machine learning system has been trained, the diffraction signals can be transformed back.
  • the dimensions of the diffraction signals can be divided into two or more partitions.
  • a machine learning system is configured and trained for each of the partitions. For example, assume the dimensions are divided into a first partition and a second partition. Thus, in this example, a first machine learning system is configured and trained for the first partition, and a second machine learning system is configured and trained for the second partition.
  • partitioning the dimensions and using multiple machine learning systems is that parallel processing can be used (e.g., the two machine learning systems can be trained and used in parallel).
  • Another advantage is that each of the machine learning systems may be more accurate as to their respective partitions than a single machine learning system.
  • a diffraction signal is generated using the machine learning system.
  • the generated diffraction signal is compared with the diffraction signal from the set of diffraction signals that corresponds to the profile.
  • 406 and 408 are repeated with another profile from the set of profiles used as the training input data.
  • the training process is terminated.
  • training process 400 can include the use of an optimization technique, such as gradient descent, linear programming, quadratic programming, simulated annealing, Marquardt-Levenberg algorithm, and the like. Additionally, training process 400 can be performed as a batch process. For a more detailed description of a batch process, see “Neural Networks” by Simon Haykin, which has been cited above.
  • training process 400 depicted in FIG. 4 illustrates a back-propagation algorithm.
  • various training algorithms can be used, such as radial basis network, support vector, kernel regression, and the like.
  • an exemplary process 500 is depicted for testing a machine learning system.
  • the machine learning system can be tested to confirm that it has been properly trained. It should be recognized, however, that this testing process can be omitted in some applications.
  • a set of testing input data is obtained.
  • a set of testing output data is obtained.
  • the testing input data includes a set of profiles, and the testing output data includes a set of diffraction signals.
  • the set of testing input data and set of testing output data can be obtained using the same process and techniques described above during the training process.
  • the set of testing input data and set of testing output data can be the same as or a subset of the training input data and training output data.
  • the set of testing input data and set of testing out data can be different than the training input data and training output data.
  • a diffraction signal is generated using the machine learning system.
  • the generated diffraction signal is compared with the diffraction signal from the set of diffraction signals in the testing output data that corresponds to the profile.
  • the machine learning system is re-trained.
  • the training process can be adjusted. For example, the selection and number of the training input and output variables can be adjusted. Additionally, the machine learning system can be adjusted. For example, when the machine learning system is a neural network, as described above, the number of hidden nodes can be adjusted.
  • the testing process is terminated.
  • ERM empirical risk minimization
  • the machine learning system can be used to generate diffraction signals for use in analyzing a structure formed on a semiconductor wafer. Again, it should be noted that the testing process can be omitted in some applications.
  • an exemplary process 600 is depicted for using a machine learning system to examine a structure formed on a semiconductor wafer.
  • a measured diffraction signal of the structure is obtained by using a metrology device.
  • a generated diffraction signal is obtained using the machine learning system.
  • the diffraction signals are compared.
  • a feature of the structure is determined based on the comparison of the measured and generated diffraction signals.
  • a profile corresponding to the generated diffraction signal is used as an input to the machine learning system to generate the generated diffraction signal.
  • the profile is characterized by one or more parameters.
  • an exemplary process 700 is depicted for using a machine learning system in a library-based process.
  • a library of diffraction signals are generated using the machine learning system. More particularly, the library of diffraction signals is generated by inputting a range of profiles into the machine learning system.
  • a measured diffraction signal is obtained using a metrology device, such as an ellipsometer, reflectometer, and the like.
  • the measured diffraction signal is compared to the diffraction signals in the library of diffraction signals generated using the machine learning system.
  • a feature of the structure is determined using the profile corresponding to the matching diffraction signal from the library of diffraction signals.
  • an exemplary system 800 is depicted for using a machine learning system in a library-based system.
  • library 116 is generated using machine learning system 118 .
  • Library 116 is then used by processing module 114 to compare the diffraction signals in library 116 to measured diffraction signals obtained from a metrology device 802 , such as an ellipsometer, a reflectometer, and the like.
  • a metrology device 802 such as an ellipsometer, a reflectometer, and the like.
  • machine learning system 118 is depicted as a separate unit in FIG. 8, machine learning system 118 can be integrated as a component of processing module 114 .
  • machine learning system 118 can be connected to processing module 114 to transmit library 116 to processing module 114 , such as through a network connection.
  • library 116 can be stored on a portable storage medium and physically transported to processing module 114 .
  • processing module 114 can be coupled to a semiconductor fabrication unit 804 that is configured to perform one or more fabrication steps. It should be recognized, however, that the metrology system can operate as a stand-alone system in addition to being integrated with semiconductor fabrication unit 804 .
  • an exemplary process 900 is depicted for using a machine learning system in a regression-based process.
  • a measured diffraction signal is obtained using a metrology device, such as an ellipsometer, reflectometer, atomic force microscope (AFM), scanning electron microscope (SEM),and the like.
  • a generated diffraction signal is obtained using the machine learning system.
  • the two diffraction signals are compared. When the two diffraction signals do not match within a predetermined matching criterion, 904 and 906 are repeated with another diffraction signal generated in 904 .
  • This process is iterated until a match is found, meaning that the generated and measured diffraction signals match within the predetermined matching criterion.
  • the profile corresponding to the matching diffraction signal is assumed to correspond to the actual profile of the structure being examined.
  • the profile and the parameters that characterize the profile can be used to determine a feature of the structure.
  • an exemplary system 1000 is depicted for using a machine learning system in a regression-based system.
  • an optimizer 1002 receives the measured diffraction signal as an input from metrology device 802 .
  • Optimizer 1002 receives the generated diffraction signal as an input from machine learning system 118 .
  • Optimizer 1002 compares the generated and the measured diffraction signals. When the generated and measured diffraction signals do match, optimizer 1002 outputs the profile corresponding to the matching generated diffraction signal. When the generated and measured diffraction signals do not match within a predetermined matching criterion, optimizer 1002 outputs a signal to machine learning system 118 to generate another diffraction signal. This process is iterated until a match is found, meaning that the generated and measured diffraction signals match within the predetermined matching criterion.
  • an optimization technique is used to reduce the number of iterations needed to arrive at a match. More particularly, the aim of an optimization problem is to find a best solution among several possible solutions, where the best solution can be quantified by associating a cost function. In other words, for a given problem under a given cost metric, the task is to find a solution with the least cost. Thus, in the present exemplary application, the task is to find the profile with a corresponding diffraction signal that produces the least cost (under a given cost metric) with respect to the given measured diffraction signal.
  • optimization techniques which are broadly classified into two categories (i.e., global and local), are known and can be used, such as gradient descent, linear programming, quadratic programming, simulated annealing, Marquardt-Levenberg algorithm, and the like.
  • global and local optimization techniques see “Numerical Recipes in C”, by William H. Press, Saul A. Teukolsky, William T. Vetterling and Brian P. Flannery, Second Edition, Cambridge, which is incorporated herein by reference.
  • a library of diffraction signals can be generated as part of a regression-based process. More particularly, when a match has been made, meaning that the generated diffraction signal and the measured diffraction signal match within the matching criterion, a library of diffraction signals can be generated around the matching profile. Generally, the library of diffraction signals generated as part of the regression-based process is smaller than the library that is generated as part of the library-based process described above.
  • the library of diffraction signals generated as part of a regression-based process and the library generated as part of a library-based process described above can be used in an interpolation process, where a solution is derived between two entries in the library.
  • an interpolation process see U.S. patent application Ser. No. 10/075,904, titled PROFILE REFINEMENT FOR INTEGRATED CIRCUIT METROLOGY, filed on Feb. 12, 2002, which is incorporated herein by reference in its entirety.
  • machine learning system 118 can be configured to operate with a non-optical metrology device, such as an atomic force microscope (AFM), scanning electron microscopes (SEM), and the like, or a combination of an optical and a non-optical metrology device.
  • a non-optical metrology device such as an atomic force microscope (AFM), scanning electron microscopes (SEM), and the like
  • machine learning system 118 can generate various types of diffraction signals corresponding to the type of metrology device used.
  • the diffraction signal generated by machine learning system 118 is a SEM signal, such as two-dimensional images or SEM traces.
  • the diffraction signal generated can include characteristic functions of the signal used by the metrology device.
  • various order derivatives e.g., first order, second order . . . n th order derivatives
  • Marquardt-Levenberg algorithm to optimize the training process.

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US10/608,300 US20040267397A1 (en) 2003-06-27 2003-06-27 Optical metrology of structures formed on semiconductor wafer using machine learning systems
PCT/US2004/020682 WO2005003911A2 (en) 2003-06-27 2004-06-25 Optical metrology of structures formed on semiconductor wafers using machine learning systems
KR1020057024949A KR101059427B1 (ko) 2003-06-27 2004-06-25 기계학습시스템을 이용한 반도체 웨이퍼 상에 형성된구조물의 광학적 계측
DE112004001001T DE112004001001T5 (de) 2003-06-27 2004-06-25 Optische Messung von auf Halbleiterwafern gebildeten Strukturen unter Verwendung von Maschinenlernsystemen
CNB2004800149754A CN100418083C (zh) 2003-06-27 2004-06-25 使用机器学习系统的对在半导体晶片上形成的结构的光学计量
JP2006517724A JP4589315B2 (ja) 2003-06-27 2004-06-25 機械学習システムを用いた半導体ウェハ上に形成された構造の光学測定
US12/399,011 US7831528B2 (en) 2003-06-27 2009-03-05 Optical metrology of structures formed on semiconductor wafers using machine learning systems

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