Patent application number | Description | Published |
20080212080 | MEASURING A PROCESS PARAMETER OF A SEMICONDUCTOR FABRICATION PROCESS USING OPTICAL METROLOGY - To measure a process parameter of a semiconductor fabrication process, the fabrication process is performed on a first area using a first value of the process parameter. The fabrication process is performed on a second area using a second value of the process parameter. A first measurement of the first area is obtained using an optical metrology tool. A second measurement of the second area is obtained using the optical metrology tool. One or more optical properties of the first area are determined based on the first measurement. One or more optical properties of the second area are determined based on the second measurement. The fabrication process is performed on a third area. A third measurement of the third area is obtained using the optical metrology tool. A third value of the process parameter is determined based on the third measurement and a relationship between the determined optical properties of the first and second areas. | 09-04-2008 |
20080285054 | OPTICAL METROLOGY OPTIMIZATION FOR REPETITIVE STRUCTURES - An optical metrology model for a structure to be formed on a wafer is developed by characterizing a top-view profile and a cross-sectional view profile of the structure using profile parameters. The profile parameters of the top-view profile and the cross-sectional view profile are integrated together into the optical metrology model. The profile parameters of the optical metrology model are saved. | 11-20-2008 |
20090094001 | TRANSFORMING METROLOGY DATA FROM A SEMICONDUCTOR TREATMENT SYSTEM USING MULTIVARIATE ANALYSIS - Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables. | 04-09-2009 |
20110307424 | DETERMINATION OF TRAINING SET SIZE FOR A MACHINE LEARNING SYSTEM - Automated determination of a number of profiles for a training data set to be used in training a machine learning system for generating target function information from modeled profile parameters. In one embodiment, a first principal component analysis (PCA) is performed on a training data set, and a second PCA is performed on a combined data set which includes the training data set and a test data set. A test data set estimate is generated based on the first PCA transform and the second PCA matrix. The size of error between the test data set and the test data set estimate is used to determine whether a number of profiles associated with the training data set is sufficiently large for training a machine learning system to generate a library of spectral information. | 12-15-2011 |
20120199287 | TRANSFORMING METROLOGY DATA FROM A SEMICONDUCTOR TREATMENT SYSTEM USING MULTIVARIATE ANALYSIS - Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables. | 08-09-2012 |
20120226644 | Accurate and Fast Neural network Training for Library-Based Critical Dimension (CD) Metrology - Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described. | 09-06-2012 |
20140032463 | ACCURATE AND FAST NEURAL NETWORK TRAINING FOR LIBRARY-BASED CRITICAL DIMENSION (CD) METROLOGY - Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described. | 01-30-2014 |
20140106477 | METHOD OF ENDPOINT DETECTION OF PLASMA ETCHING PROCESS USING MULTIVARIATE ANALYSIS - Disclosed is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. Optical emission spectroscopy (OES) data are acquired by a spectrometer attached to a plasma etch processing tool. The acquired time-evolving spectral data are first filtered and demeaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. A functional form incorporating multiple trends may be used to more precisely determine the endpoint of an etch process. A method for calculating principal component weights prior to actual etching, based on OES data collected from previous etch processing, is disclosed, which method facilitates rapid calculation of trends and functional forms involving multiple trends, for efficient and accurate in-line determination of etch process endpoint. | 04-17-2014 |