Modified nano-structure feature size extraction method based on support vector machine

Abstract

The invention discloses a modified nano-structure feature size extraction method based on a support vector machine. The modified nano-structure feature size extraction method comprises the following steps: identifying the value range of each parameter to be extracted, so as to generate an excton spectrum database; training the support vector machines by utilizing a training spectrum and a support vector machine training network; repeatedly training multiple support vector machines on each parameter to be extracted by utilizing the training spectrum, wherein the training termination conditions of the support vector machines are different; mapping a measuring spectrum by utilizing the multiple support vector machines; finding out the mapping result which presents morest times in the mapping results of all the support vector machines; regarding the mapping result as a value taking interval which can most possibly present; establishing the excton sub spectrum database; finding out a simulation spectrum which is the most similar with the measuring spectrum; and regarding the simulation spectrum as a parameter value of a structure to be measured. With the adoption of the modified nano-structure feature size extraction method, accurate rapid extraction of nano-structure parameters such as feature line width, height and side wall angles can be realized, the process is simple to realize, and robustness extraction of the nano-structure feature size is further realized.

Description

technical field [0001] The invention belongs to the field of semiconductor scattering optics measurement, and in particular relates to an improved method for rapidly extracting characteristic dimensions of nanostructures based on support vector machines. Great extraction results. It is suitable for fast and accurate measurement of the characteristic size of semiconductor nanostructures. Background technique [0002] During the nanomanufacturing process, rapid, non-destructive, and low-cost measurement of the three-dimensional shape parameters of nanostructures is of great significance for maintaining the reliability, consistency, economy, and scale production of nanoscale products. The three-dimensional morphology of these nanostructures to be measured includes characteristic line width (characteristic size), height, period and side wall angle, etc. [0003] Scatterometry is a device for measuring the characteristic size of semiconductor nanostructures based on optical pri...

AI Technical Summary

Problems solved by technology

However, the result of this method, that is, whether the global nearest neighbor simulation spectrum can be searched in the mapped sub-spectrum database, depends on the mapping accuracy of the classifier. Although the support vector machine has the best generalization ability in theory, However, a single support vector machine still cannot guarantee a high classification accuracy

Images