HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY (Wuhan, CN)

Method to extract the crucial dimension of a semiconductor structure. The method consists of: 1.) the determination of a range for each parameter that is to be extracted and generating an electronic spectra database using training spectra and support machine (SVM) training networks to aid in the training of SVMs; 2) making use of the SVMs after training to map measured spectra to produce a matching electronic spectra database; and 3) employing a search algorithm to search for an optimal simulation spectrum within the corresponding electronic spectra database, simulation parameters matching the simulation spectrum being the critical size of the nanostructure that needs to be extracted.

1. Field of the Invention

The invention is related to optical scattering and semiconductor measurement field, and, more specifically it relates to a method of extracting a critical dimension of the semiconductor nanostructure.

2. Description of Related Art

It is important to measure 3D morphology parameters, like feature linewidth, sideswall angles and cycles, to ensure repeatability, operability and the flexibility of the manufacturing process.

In the semiconductor and optical measurement field, an optical scatterometer is the most popular-used instrument to measure important dimensions. Forward optical modeling as well as reverse looking are two methods for measuring the optical scatterometer. This is where you can carry out optical scattering field simulations on the models of geometric nanostructures that are to be studied. Reverse-selecting is the continuous comparison of the measured spectra to the simulation spectraand the parameters of a model corresponding to a simulation spectrum with the greatest degree of similarity are the elements of the nanostructures to be measured. The most popular-used method during reverse seeking of the optical scatterometer is alibrary-matching-based method. This technique involves creating the database of simulation spectra to be used in an underlying structure model to determine the spectrum of that database corresponding with an individual parameter value is sought out for the most comparable simulation spectrum to that model according to an evaluation function. The parameter value of an model is the amount of the simulation spectrum. The spectradatabase may be vast and includes a lot of simulation spectra. It can result in an upward progression of geometrical form in which the database includes many more simulation spectra as well as every spectrum that is independent corresponds to the model as determined by the parameter value. Finally, an evaluation function searches for a simulation spectrum most similar to the model according to the parameter value for a model. The parameter value of a model corresponding to the spectrum of simulation is the structure to be measured. To meet requirements for real-time feature and rapidity in industries, and to implement fast mapping of measured spectra in a large-scale spectra database, a new mapping method needs to be launched, and an old full-librarysearching method needs to be discarded.

Library-mapping-based extraction of geometric parameters of a nano structure includes establishment of a simulation database, and searching in a database. The process of searching in a database involves the search for a spectrum that is most similar to ameasured spectrum within the database, following a set of rules and is the most common type of maximum proximity searching. The traditional methods to solve the problem of maximum proximity include a direct whole-library search method, a k-d treemethod, an analysis of clusters, a local sensitivity hashing and so on. Other than the whole-library search method, however it is not possible to achieve an optimal solution. This is due to the techniques used to solve problems such as the most-similar spectra hunts are not able produce a good global solution. Because spectra have non-obvious characteristics, these methods employ one or more parameters to determine the search. The GPU (Graphic Processing Unit) is utilized to store the most similar simulation spectra within a database. The GPU (Graphic Processing Unit) is an acceleration device specially designed to handle images. It has a faster processing speed for data, and superior data processing capabilities, and more concurrent computation capabilities over CPU. By arranging multiple simulation spectrums into an image matrix that indicates spectra it is possible to quickly map the spectra of the spectrum and determine geometric parameters. However, problems with this method are that, with further expansion ofthe simulation spectra database, more powerful and high-efficient GPUs must be used, which limits scalability of this category of hardware-acceleration-based searching method.

A method to extract a critical dimension from the structure of a semiconductor is an objective of the invention. It can be used to rapidly and precisely extract feature linewidth,height and sidewall angles. This process is simple.

To attain the above goal, in accordance with one embodiment of the invention, there is provided a method for extraction of a critical dimension in a semiconductor nanostructure, the method comprises the steps of: (1) the determination of a rangefor each parameter to be extracted, creating an electronic spectra database using training spectra as well as support vector machine (SVM) training networks to aid in the training of SVMs; (2) employing the SVMs after training to map measured spectrato create a digital spectra database; and (3) employing a searching algorithm to search for an optimum simulation spectrum in the electronic spectra database. The simulation parameters that correspond to the spectrum of simulation being the crucial dimension of the semiconductor nanostructure to be extracted.

Electronic spectra databases may be obtained from a class according to this model by: dividing the ranges of values of each parameter into several subvalue ranges, selecting one subvalue range for each subvalue range, and creating a subparameter mix. Then, using forward optical modeling software to generate a simulation spectrum of the values that are discrete. This simulation spectrum corresponds to all discrete points in every subparameter combination. values.

In a class of this kind, during the process of removing the training spectra, every parameter extracted is associated with a SVM and the number of categories that are contained in an output of the SVM is determined by that of sub-ranges dividedfrom the value range of each parameter to be extracted, each category is identified with an unique number, and is can be used to identify the sub-range. Each sub-range corresponds to a spectra of training set, and each training spectra set represents a sub-range, by randomlychoosing several discrete values within a sub-range, and generating a simulation spectrum for each point on the discrete side using the forward optical modeling program. which is referred to as a simulation spectra collection or the training spectra set is produced, and all of the training spectra containedin training spectra sets that are corresponding to the output ends of the SVM are the training spectra that are that are required by the SVM.

A class in this embodiment has multiple categories at the output of an SVM. Each category represents a subrange of the value range the parameter is attempting to extract. The output of the SVM has a category which indicates a subrange of the value range.

This class describes how to search for the most optimal spectrum for simulation. It involves placing all the electronic spectrum into a matrix with each row being a simulation spectrum, and the simulation spectrumuniquelycorresponding to group parameter computing evaluation function values with each simulation spectra , and the spectrum that is measured in the matrix beginning at the top according to an evaluation function searching using a search algorithm or sorting algorithm for the value of the minimum evaluation function, the simulation spectrum that is the one with the lowest evaluation value being the best simulation spectrum

The advantages of the method of extracting a critical dimension of a semiconductor nanostructure are summarized below. Compared with a library-mapping-based method for extracting feature dimensions in the prior art, the invention is capable ofmapping the measured spectra into a small-range sub-database by adding a process of off-line training using a SVM classifier, and time spent on searching in the sub-database is far less than that on most-similar spectra searching in a large database. Inaddition, by increasing the number of categories within each classifier (namely splitting the value range of each parameter into multiple sub-ranges) A smaller sub-database is possible to be created that further speeds up the extraction of parameters, and the invention also provides a an automated and controlled extraction speed of parameters.

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