Method to extract the essential dimension of the semiconductor structure. The procedure consists of: 1.) determining a value for each parameter to be extracted, whereby creating an electronic spectra database, and employing training spectra and support machine (SVM) training networks for training of SVMs 2.) making use of the SVMs after training to map measured spectra into a digital data base of spectra; and) employing a searching algorithm to search for the most optimal simulation spectrum in the electronic spectra database, simulation parameters that match the simulation spectrum being the crucial dimension of the semiconductor nanostructure to be extracted.

1. Field of the Invention

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

2. Description of the Related Art

It is essential to quantify 3D morphology parameters, such as feature linewidth sideswall angles, and cycles in order to improve repeatability, operability and the flexibility of the manufacturing process.

An optical scatterometer, employed in measurements of semiconductors and optical structures is the most widely-used instrument to measure critical dimensions. The measurement of an optical scatterometer involves forward optical modeling as well as reverse seeking.Forward optical modeling is used to conduct optical scattering field simulation on the geometric models of nano structures to be measured, whereby extracting simulation spectrum. Reverse seeking includes continuously comparing the measured spectra with thesimulation spectrum, as well as the parameters of a model corresponding to the simulation spectrum having the highest degree of similarity are characteristics of the nanostructures to be observed. The most popular-used method during reverse seeking of the optical scatterometer is alibrary-matching-based method. This method involves constituting a simulation spectra database for a structure model that is to be measured, each independent spectrum of the database that corresponds to a model determined by an input parameter in search of the most closely-matched simulation spectrum to each measured spectrum in the database according to an evaluation function, a parameter value of the model corresponding to the spectrum of simulation being the same as the structure to be measured. However, the spectradatabase often has a lot of simulation spectra and the number of the spectra is to grow in a geometric progression with increase of parameters of the nano structures, expansion of the parameters used in simulation, and rise in requirement for accuracyof parameters to be extracted. 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. A database search involves looking for the simulation spectrum which is the closest to the spectrum measured in the database. This is also known as the maximum proximity search. The traditional methods to solve the problem of maximum proximity include a direct whole-library searching method, a k-d treemethod, a clustering analysis method and local sensitivity hashing, and more. However, no optimal result can be expected when the above-mentioned methods with the exception of the whole-library searching method can be used to solve problems like most-similar spectra search. Because spectra can have non-obvious properties, these methods use several parameters to search. A GPU (Graphic Processing Unit) can also be used to determine the mapping of most similar simulation data spectra within databases. The GPU is a hardware-accelerated module specifically designed to process images and offering a greater processing speed, better data processing, and capacity for concurrent computation in comparison to a CPU. Fast mapping of spectra can be achieved by arranging multiple simulation spectra into a matrix denoting the images of spectra, and hence the extraction of geometric parameters is quick. 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.

With regard to the above-described problems, it is the goal of this invention to present the method of extracting a critical dimension of the semiconductor nanostructure that is capable of providing accurate and rapid extraction of feature linewidth, height and sidewall angles, and offering a straightforward process.

In one particular embodiment of the invention, an approach is described to determine the critical dimensions of the semiconductor structure. The method comprises steps such as (1) determining a range for each parameter to be extracted; (2) using SVMs to map the observed spectrum to an electronic database (3) using an algorithm for searching the electronic databases to determine the most optimal simulation spectrum, using simulation parameters that are in line with the spectrum of simulation being the most important dimension to be taken.

Electronic spectra databases can be obtained from a class according to this model by dividing the range of values for each parameter into multiple subvalue ranges. Then, choosing a subvalue range from each range, and then generating a subparameter combination. In the end using forward optical modeling , you can create a simulation spectrum of the values that are discrete. The simulation spectrum is corresponding to all discrete points in each combination of subparameter values.

This class will show you how to extract training spectrum from an SVM. Every parameter assigned a SVM. The number of categories at an output end is determined by the number of sub-ranges. Each category is identified by a unique number. A training set or simulation spectra are created. The training spectra of all training sets correspond to the output ends of all.

A class according to this embodiment has multiple categories at the output end of an SVM. Each category represents an element of a parameter that is a sub-range to extract. The output of the SVM has a category which specifies a sub-range of a parameter to extract. Sub-ranges are defined through mapping an SVM to each parameter.

In this embodiment, the searching for an optimum simulation spectrum involves placing all spectra from every electronic spectra database into a matrix each row of the matrix being the simulation spectrum and the simulation spectrum that is unique in corresponding to a specific group of parameter values; calculating evaluation function values for every simulation spectrum and the measured spectra of the matrix from top to bottom using an evaluation function; and searching for a value of the minimum evaluation function using the search algorithm or a sorting algorithm or a sorting algorithm. A simulation spectrum that corresponds to the value of the evaluation function that is the minimum as the most optimal simulation spectrum.

Advantages of the method for finding a critical dimension in the nanostructure of a semiconductor 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. The invention also permits the creation of a smaller sub-database by increasing the number of sub-ranges for each category. This makes it easier to extract of parameters’ values.

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